Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins
Open accessAbstract
One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans' biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a "systems-wide" functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.
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When AF2Complex was applied to a previously defined gold standard interaction set in E. coli, it found that many protein pairs are likely due to associations in large assemblies that are not necessarily in direct physical contact.
“When AF2Complex was applied to a previously defined “gold standard” interaction set in E. coli32, it found that many protein pairs are likely due to associations in large assemblies that are not necessarily in direct physical contact.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [32]
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Cited by
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Phenotypic Landscape of a Bacterial Cell
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A Protein Complex Network of Drosophila melanogaster
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Koo, B., Kritikos, G., Farelli, J. D., Todor, H., Tong, K., Kimsey, H. H., Wapinski, I. N., Galardini, M., Cabal, A., Peters, J. M., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.12.013 · Cited by 584 · see citing works
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Horizontal Transfer, Not Duplication, Drives the Expansion of Protein Families in Prokaryotes
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BackgroundProteome organization in a genome-reduced bacterium
Kühner, S., van Noort, V., Betts, M. J., Leo-Macias, A., Batisse, C., Rode, M., Yamada, T., Maier, T., Bader, S., Beltran-Alvarez, P., et al. (2009) · Science · doi:10.1126/science.1176343 · Cited by 471 · see citing works
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“coli covering hundreds of protein products of previously uncharacterized ‘orphan’ bacterial genes was recently published by our group [43].”
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“MG1655 (NC_000913), a related enteric strain on which extensive experimental and functional assays have been conducted [51,52].”
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Mohni, K. N., Wessel, S. R., Zhao, R., Florian, A. C., Luzwick, J. W., Layden, H. M., Eichman, B. F., Thompson, P. S., Mehta, K. P. M., & Cortez, D. (2018) · Cell · doi:10.1016/j.cell.2018.10.055 · Cited by 183 · see citing works
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iPfam: a database of protein family and domain interactions found in the Protein Data Bank
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Discovery and significance of protein-protein interactions in health and disease
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“Membrane-bound proteins and transporters are particularly hard to characterize with certainty (43), but high-throughput methods might change that, as they already have for enzymatic assays (44,45), gene-environment networks (46) and protein–protein interactions (16).”
BackgroundInfluentialMetabolic and Transcriptional Response to Cofactor Perturbations in Escherichia coli
Holm, A. K., Blank, L. M., Oldiges, M., Schmid, A., Solem, C., Jensen, P. R., & Vemuri, G. N. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m109.095570 · Cited by 141 · see citing works
Global landscape of cell envelope protein complexes in Escherichia coli
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“Although other protein targets were found, these studies identified a putative MreB–FtsZ interaction (Butland et al , 2005; Hu et al , 2009).”
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“detected but the sites of interaction are not known (Hu et al., 2009).”
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“coli genes are conserved among a majority of bacteria, particularly among closely related cproteobacterial species [2,10], we investigated the evolutionary significance of the putative functional associations detected by eSGA in E.”
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“coli genome, and that over a third of these have an unknown or poorly understood function [12], it is likely that many factors important for modulating envelope assembly remain to be identified.”
BackgroundFast integration of heterogeneous data sources for predicting gene function with limited annotation
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Multiplexed gene synthesis in emulsions for exploring protein functional landscapes
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“database to derive a network of co-expressed genes based on correlation; and functional connections inferred by genomic context methods were computed as reported before [14].”
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“The uncharacterized proteins comprise a substantial percentage of the predicted proteins in many genomes, and some of these proteins are of no clear sequence or structural similarity to a protein of known function [31,32].”
BackgroundRecent advances in charting protein–protein interaction: mass spectrometry-based approaches
Gavin, A., Maeda, K., & Kühner, S. (2010) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.09.007 · Cited by 120 · see citing works
Estimation of Simultaneously Sparse and Low Rank MatricesPreprint
Richard, É., Savalle, P.-A., & Vayatis, N. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6474 · Cited by 118 · see citing works
“We use data from (Hu et al., 2009), in which protein interactions in Escherichia coli bacteria are scored by strength in [0, 2].”
MethodIt's the machine that matters: Predicting gene function and phenotype from protein networks
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BackgroundGenomewide landscape of gene–metabolome associations in Escherichia coli
Fuhrer, T., Zampieri, M., Sévin, D. C., Sauer, U., & Zamboni, N. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167150 · Cited by 114 · see citing works
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Protein–protein interaction networks: the puzzling riches
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Bacterial protein networks: properties and functions
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High Expression Hampers Horizontal Gene Transfer
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High-resolution network biology: connecting sequence with function
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λ Recombination and Recombineering
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The Sm‐like RNA chaperone Hfq mediates transcription antitermination at Rho‐dependent terminators
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An RNA degradosome assembly in Caulobacter crescentus
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“In addition to the canonical components, the degradosome is also associated with other proteins in sub-stoichiometric amounts, including the RNA chaperone Hfq, ribosomal proteins, polyphosphate kinase and protein chaperones (4,7,17,18).”
BackgroundEPIC: software toolkit for elution profile-based inference of protein complexes
Hu, L. Z., Goebels, F., Tan, J. H., Wolf, E. J., Kuzmanov, U., Wan, C., Phanse, S., Xu, C., Schertzberg, M., Fraser, A. G., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0461-4 · Cited by 94 · see citing works
Self-citationTowards a detailed atlas of protein–protein interactions
Mosca, R., Pons, T., Ceol, A. G. M., Valencia, A., & Aloy, P. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.07.005 · Cited by 91 · see citing works
The Modular Organization of Protein Interactions in Escherichia coli
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“Also shown are results for a random network constructed with the same proteins and topology as the combined network [27] (see Suppl.”
BackgroundMethodInfluentialA role for tetrahydrofolates in the metabolism of iron-sulfur clusters in all domains of life
Waller, J. C., Alvarez, S., Naponelli, V., Lara‐Núñez, A., Blaby, I. K., Silva, V. D., Ziemak, M. J., Vickers, T. J., Beverley, S. M., Edison, A. S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911586107 · Cited by 93 · see citing works
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“Large scale PPI studies using a high throughput and systematic AP-MS approach have been performed for Escherichia coli (11, 12) and Saccharomyces cerevisiae (13– 15).”
BackgroundSelf-citationReconstruction and Application of Protein–Protein Interaction Network
Hao, T., Peng, W., Wang, Q., Wang, B., & Sun, J. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17060907 · Cited by 87 · see citing works
“coli [62], and Mycoplasma pneumonia [63].”
Profiling the Escherichia coli membrane protein interactome captured in Peptidisc libraries
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Synergistic use of plant-prokaryote comparative genomics for functional annotations
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“Comparing results to those from automated functional prediction platforms Several recently developed platforms seek to automatically integrate comparative genomics, high-throughput experimental data, and literature reports to make gene function predictions [46,62].”
BackgroundMethodResultThe emergence of proteome-wide technologies: systematic analysis of proteins comes of age
Breker, M., & Schuldiner, M. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3821 · Cited by 82 · see citing works
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“coli (Hu et al., 2009).”
Self-citationMembrane transport metabolons.
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The tandem affinity purification technology: an overview
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The protein organization of a red blood cell
Sae-Lee, W., McCafferty, C. L., Verbeke, E. J., Havugimana, P. C., Papoulas, O., McWhite, C. D., Houser, J. R., Vanuytsel, K., Murphy, G. J., Drew, K., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111103 · Cited by 78 · see citing works
Self-citationPhyletic distribution and conservation of the bacterial transcription termination factor Rho
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“, 2011b), and co-purifies with a host of other factors (Butland et al., 2005; Hu et al., 2009) that vary with growth conditions to include, for instance, RNA degradosome components PNPase and RNase E (Tuckerman et al.”
BackgroundPhage Recombinases and Their Applications
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“This technology has been applied to more than 1000 orphaned genes, allowing many of the genes to be assigned to functional classes (Hu et al., 2009).”
MethodXiong, X., Frank, D. N., Robertson, C. E., Hung, S. S., Markle, J., Canty, A. J., McCoy, K. D., Macpherson, A. J., Poussier, P., Danska, J. S., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036009 · Cited by 74 · see citing works
“Compendia of physical and functional interactions are now available for bacteria that can be exploited as scaffolds onto which RNA-Seq data may be mapped [48–53].”
BackgroundTransient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity
Bhattacharyya, S., Bershtein, S., Yan, J., Argun, T., Gilson, A. I., Trauger, S. A., & Shakhnovich, E. I. (2016) · eLife · doi:10.7554/elife.20309 · Cited by 71 · see citing works
Bacteriophage Protein–Protein Interactions
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“If two different laboratories purify the same protein complexes using different methods, the results may differ even more dramatically (Hu et al., 2009).”
BackgroundGrad‐seq in a Gram‐positive bacterium reveals exonucleolytic sRNA activation in competence control
Hör, J., Garriss, G., Giorgio, S. D., Hack, L., Vanselow, J. T., Förstner, K. U., Schlösser, A., Henriques‐Normark, B., & Vogel, J. (2020) · The EMBO Journal · doi:10.15252/embj.2019103852 · Cited by 69 · see citing works
“Importantly, Grad-seq drafts a global landscape of the major protein complexes in vivo in a single experiment, without the massive epitope-tagging of individual proteins in previous binary protein – protein interactome studies (Hu et al , 2009; Rajagopala et al , 2014).”
Exploring bacterial interspecific interactions for discovery of novel antimicrobial compounds
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“(18) combined proteomics and genomic context (GC) analysis tools to map orphans to specific biological processes.”
BackgroundCombining CRISPRi and metabolomics for functional annotation of compound libraries
Anglada-Girotto, M., Handschin, G., Ortmayr, K., Campos, A. I., Gillet, L., Manfredi, P., Mulholland, C. V., Berney, M., Jenal, U., Picotti, P., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-00970-3 · Cited by 60 · see citing works
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A Soluble Metabolon Synthesizes the Isoprenoid Lipid Ubiquinone
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A Second-generation Protein–Protein Interaction Network of Helicobacter pylori
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Self-citationAcinetobacter baylyi ADP1 as a model for metabolic system biology
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Novel RpoS-Dependent Mechanisms Strengthen the Envelope Permeability Barrier during Stationary Phase
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“S6), and we are further investigating the possibility of functional interactions between YhdP and YdgH.”
BackgroundInfluentialRevisiting the y-ome of Escherichia coli
Moore, L. R., Caspi, R., Boyd, D. H., Berkmen, M., Mackie, A. M., Paley, S., & Karp, P. D. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae857 · Cited by 55 · see citing works
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“In Escherichia coli and yeast, homologous recombination allows for fast and efficient endogenous tag integration, and subsequent expression of the tagged protein at physiological levels under the control of their endogenous promoter [10,43].”
BackgroundThe Codon Usage of Lowly Expressed Genes Is Subject to Natural Selection
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“…gene or the synonymous mutated bla gene, was linearly transformed (Datsenko and Wanner 2000) into the chromosomal region 3635970–3636206 of previously generated E. coli K12 MG1655 strains, containing antibiotic resistance cassettes (either kanamycin or chloramphenicol) (Katz and Hershberg 2013).”
BackgroundChaperonin-Dependent Accelerated Substitution Rates in Prokaryotes
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“Conversely, proteins that have a more peripheral role within the cell are more tolerant to increased evolutionary rates and are protected from slightly deleterious mutations by the buffering effect of the GroEL/GroES chaperone.”
BackgroundImproved sparse low-rank matrix estimation
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“Protein interactions in the ‘Escherichia coli’ bacteria, scored by strength in [0 , 2], were studied in [30].”
MethodBabu, M., Butl, G., Pogoutse, O., Li, J., Greenblatt, J., & Emili, A. (2009) · Methods in molecular biology · doi:10.1007/978-1-60761-157-8_22 · Cited by 51 · see citing works
Self-citationProperties and identification of antibiotic drug targets
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“coli proteins, giving many new functional assignments [11].”
BackgroundKumar, A., Beloglazova, N. G., Bundalovic-Torma, C., Phanse, S., Deineko, V., Gagarinova, A. G., Musso, G. A., Vlasblom, J., Lemak, S., Hooshyar, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.060 · Cited by 51 · see citing works
Self-citationGagarinova, A. G., Stewart, G., Samanfar, B., Phanse, S., White, C. A., Aoki, H., Deineko, V., Beloglazova, N. G., Yakunin, A. F., Golshani, A., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.040 · Cited by 48 · see citing works
“Subsequently, each dataset was partitioned into local sub-networks by either applying the Markov clustering algorithm as described previously (see Hu et al., 2009 and references therein) or by isolating the first neighbor network of each gene.”
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Genetic Co-Occurrence Network across Sequenced Microbes
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“To test this idea, we compared the distribution of wij for physically-binding proteins in Escherichia coli with that for arbitrary pairs of the proteins [27], and found that highly correlogous proteins are much likely to physically interact (Figure 1A).”
BackgroundResultA directed-overflow and damage-control N-glycosidase in riboflavin biosynthesis
Frelin, O., Huang, L., Hasnain, G., et al. (2014) · Biochemical Journal · doi:10.1042/bj20141237 · Cited by 49 · see citing works
Network-based function prediction and interactomics: The case for metabolic enzymes
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“…and then construct high-confidence functional linkage networks which are then used for function prediction (Bowers et al., 2004; Chen and Xu, 2004; Hu et al., 2009a; Jensen et al., 2009; Lanckriet et al., 2004; Linghu et al., 2008; Marcotte et al., 1999; Massjouni et al., 2006; Myers et al.,…”
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- Authors
- Hu, P., Janga, S. C., Babu, M., Díaz-Mejía, J. J., Butland, G., Yang, W., Pogoutse, O., Guo, X., Phanse, S., Wong, P., Chandran, S., Christopoulos, C., Nazarians-Armavil, A., Nasseri, N. K., Musso, G., Ali, M., Nazemof, N., Eroukova, V., Golshani, A., Paccanaro, A., Greenblatt, J. F., Moreno-Hagelsieb, G., & Emili, A.
- Affiliations
- University of Toronto (CA)
- MRC Laboratory of Molecular Biology (GB)
- Ottawa Institute of Systems Biology (CA)
- Royal Holloway University of London (GB)
- Corresponding author
- Emili, Andrew
- ORCID
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-04-28
- Volume
- 7
- Issue
- 4
- Pages
- e1000096
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC2672614
- OpenAlex
- W2018756605
- Cited by
- 387
- References
- 105
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 13 · 2023: 13 · 2022: 19 · 2021: 18 · 2020: 16 · 2019: 15 · 2018: 19 · 2017: 26 · 2016: 33 · 2015: 29 · 2014: 23 · 2013: 31 · 2012: 38
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Carleton University's Institutional Repository (MacOdrum Library, Carleton University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Royal Holloway Digital Repository (Royal Holloway University of London) (submittedVersion)
- Scholars Commons (Wilfrid Laurier University) (submittedVersion)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Funders
- Genome Canada
- Ontario Genomics
- Ontario Genomics Institute
- Canadian Institutes of Health Research (77639_1)
- Natural Sciences and Engineering Research Council of Canada
- Biotechnology and Biological Sciences Research Council (BB/F00964X/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Escherichia coli K-12, orphan proteins, protein-protein interaction, functional association networks, protein complexes, functional modules, protein synthesis, amino acid metabolism, biofilm formation, bacterial motility
- MeSH terms
- Escherichia coli, Genome, Bacterial, Proteome, Protein Interaction Mapping, Escherichia coli Proteins, Multiprotein Complexes
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