Find research datasets worth reusing

opencc-zeroMar 2022View details →

Dataset from: Changes in cell size and shape during 50,000 generations of experimental evolution with Escherichia coli

Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.

opencc-zeroMay 2022View details →

Data from: Single mutation makes Escherichia coli an insect mutualist

We report an experimental system in which Escherichia coli evolves into an insect mutualist. When the essential gut symbiont of the stinkbug Plautia stali was replaced by E. coli, a few survivor insects exhibited specific localization and vertical transmission of E. coli. Through trans-generational maintenance with P. stali, several hyper-mutating E. coli lines independently evolved host's high adult emergence and improved body color. Such "mutualistic" E. coli lines exhibited independent mutations disrupting the carbon catabolite repression (CCR) global transcriptional regulator. Each of the mutations reproduced the mutualistic phenotypes when introduced into wild-type E. coli, confirming that the single CCR mutations instantly make E. coli an insect mutualist. Our discovery uncovers that evolution of elaborate mutualism can proceed more easily and rapidly than conventionally envisaged.

opencc-zeroAug 2022View details →

Physiological roles of peptidoglycan carboxypeptidases DacC and DacA in Escherichia coli

Peptidoglycan (PG) is an essential bacterial architecture pivotal for shape maintenance and adaptation to osmotic stress. Although PG synthesis and modification are tightly regulated under harsh environmental stresses, few related mechanisms have been investigated. In this study, we aimed to investigate the coordinated and distinct roles of the PG carboxypeptidases DacC and DacA, in adaptation to alkaline and salt stresses and shape maintenance in Escherichia coli. We found that DacC is an alkaline PG carboxypeptidase, whose enzyme activity and protein stability are significantly enhanced under alkaline stress. Both DacC and DacA were required for bacterial growth under alkaline stress, whereas only DacA was required for the adaptation to salt stress. Under normal growth conditions, only DacA was necessary for cell shape maintenance, while under alkaline stress conditions, both DacA and DacC were necessary for cell shape maintenance, but their roles were distinct. Notably, all these roles of DacC and DacA were independent of ld-transpeptidases, which are necessary for the formation of PG 3-3 crosslinks and covalent bonds between PG and the outer membrane lipoprotein Lpp. Instead, DacC and DacA interacted with penicillin-binding proteins (PBPs), dd-transpeptidases, mostly in a C-terminal domain-dependent manner, and these interactions were necessary for most of their roles. Collectively, our results demonstrate the coordinated and distinct novel roles of PG carboxypeptidases in stress adaptation and shape maintenance and provide novel insights into the cellular functions of PG carboxypeptidases associated with PBPs.

opencc-by-4.0Sep 2022View details →

Combination of whole genome sequencing and Supervised Machine Learning provides unambiguous identification of enterohemorrhagic Escherichia coli in raw milk

These dataset are used in the "rename_list_of_groups.ipynb" notebook

opencc-zeroOct 2022View details →

Characterization of virulence factors and antibiotic resistance pattern of uropathogenic Escherichia coli strains in a tertiary care center

Background: Urinary tract infections (UTIs) are the most prevalent bacterial infection in humans. The uropathogenic E. coli (UPEC) express a wide range of virulence factors that contribute to their pathogenicity. The emergence of Multidrug resistance(MDR)-associated UTI is increasing off late. Hence this study was undertaken to monitor the distribution of virulence factors among UPEC strains and to note the antibiogram, outcome and type of associated UTI. Methods: A prospective cross-sectional time-bound study of 6 months was done on clinically significant urinary isolates of Escherichia coli. Detection of haemolysin production and serum resistance was done by phenotypic methods. Genotypic characterization of the virulence genes (papC, iutA, hlyA, cnf1) was done by multiplex PCR. Demographic data, clinical history, antibiogram and type of UTI were collected from clinical case records. Results: 75 E. coli isolates from patients with suspected urinary tract infections were included. Females had a higher preponderance of UTI (66.7%).93% of the patients were adults and the remaining 7% were from the paediatric population. 24 (32%) isolates showed haemolysis by plate haemolysis method, and all 75 (100%) isolates were serum resistant. Out of 75 isolates, 65 were positive for at least one of the four targeted genes, while the remaining 10 isolates were negative for all 4 genes. Multidrug resistance was found in 40 (53.3%) isolates. 97.4% of the UTI cases had a favourable clinical outcome at discharge. Mortality due to urosepsis was 2.6%. Conclusion: The association of hemolysin production with resistance to imipenem and norfloxacin in UPEC strains was significant. The presence of the hlyA gene is positively associated with ceftazidime resistance. Nitrofurantoin, piperacillin tazobactam and cefaperazone sulbactam maybe suitable candidates for empirical therapy of UTIs. Drugs like aminoglycosides, carbapenems and fosfomycin may be used as reserve drugs in the treatment of MDR-UTI. However, inappropriate usage can gradually increase antibiotic resistance. Hence, proper selection of antibiotics in hospitals taking into account the local antibiogram is needed to reduce the emergence of antibiotic resistance.

opencc-by-4.0May 2024View details →

Data (2) with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also: https://zenodo.org/records/11401115 Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Figure 3   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-skeletonData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/analysis/straightenedCells/2016-04-07pos2crop_straightFluorData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/analysis/straightenedCells/2016-04-07pos3crop_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-Schnitz.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-Schnitz.mat </td> </tr> </tbody> </table>   Supplemental figure with nucleoid data   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-skeletonData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/analysis/straightenedCells/2017-11-08pos1cropd_straightFluorData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/analysis/straightenedCells/2017-10-12pos1cropa2_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-Schnitz.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-Schnitz.mat </td> </tr> </tbody> </table>   Lookup data file: 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/slookup.mat

opencc-by-4.0Mar 2018View details →

Data with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also the other Zenodo record: https://zenodo.org/records/11401470   Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Currently, single cell experimental time trace data for figures 1, 2 and 4 is added. Additional data will follow.   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Description of the .mat files   These .mat files contain the lineage information, which were used to generate figures 1 and 2.   Each row corresponds to a cell from birth to division.   Most important fields are: P is its parent cell (number referring to the row of the table). E is the daughter cell after this cell divides D is the other daughter cell after this cell divides frame_nrs is in which frame this cell lived areapx is its area in pixels   Different methods were used to determine the bacterias length. For the tetracycline experiments, often the field “length_fitNew” was used, which is a higher order polynomial fitted through the bacterium. For the other stress conditions, mostly the field “length_skeleton” was used, which is the length of the skeleton of the bacteria, extrapolated until it reaches the bacterial edge (this was done because in those experiments, these bacteria often had weird shapes that couldn’t be fitted by polynomials).   The growth rate of the bacteria is determined by fitting an exponential curve through length information of multiple frames. Given the fluctuation of growth rates, sometimes bacteria grow relatively slow over a certain amount of frames, or relatively fast. Therefor, sometimes the fit is done using more or less frames. The growth rate information can be found in e.g. the parameters 'muP15_fitNew_all','muP9_skeleton_all','muP5_skeleton_all', where mu refers to growth rate, PX indicates X frames around the frame of interest were used for the fit, and fitNew_all or skeleton_all refers respectively to which length parameter was used for the fit.   Note that birth sizes and interdivision times, as wel as added length, can be calculated from the above parameters using the data structure. (See also applicable scripts.)   The data also contains more fields with information about length and size, and also fields for fluorescence data. The latter is not really applicable here.    See also the methods section of the paper for more information.   INFORMATION ABOUT THE DATA   Files for the SulA condition: <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos1crop\data\pos1crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos7crop\data\pos7crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table> If switch time is zero, recording started at the switch time.   For the temperature condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2016-03-23_FilaRecovery_asc777_42C\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 450 </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 329 </td> </tr> </tbody> </table>   Delta min tetracycline condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos1cropb\data\pos1cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropa\data\pos2cropa-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropb\data\pos2cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropc\data\pos2cropc-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos3cropa\data\pos3cropa-Schnitz.mat </td> <td> 5 </td> </tr> </tbody> </table>   Tetracycline data Only data sets 1 to 5 where used here     <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos3_long.mat </td> <td> 890.9800  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4.mat </td> <td> 404.7500         </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4_long.mat </td> <td> 0  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5.mat </td> <td> 529.7600  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5_long.mat </td> <td> 0 </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos2.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos4.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos6.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos1.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos3.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos6_long.mat </td> <td> NA </td> </tr> </tbody> </table>   Tetracycline data 2 (redundant with above)   <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2013-12-09\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 890.9800  </td> </tr> <tr> <td> 2013-09-24\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 404.7500         </td> </tr> <tr> <td> 2013-12-16\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0  </td> </tr> <tr> <td> 2013-09-24\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 529.7600  </td> </tr> <tr> <td> 2013-12-16\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table>

opencc-by-4.0Jun 2024View details →

Manufacturing of the highly active thermophile PETases PHL7 and PHL7mut3 using Escherichia coli - Dataset

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2019View details →

BSc thesis:"The dynamics and localization of three fluorescent fusions of the periplasmic chaperone Skp in Escherichia coli"

This is the full data set containing all primary and derived data generated during this thesis. The title of this thesis is: "The dynamics and localization of three fluorescent fusions of the periplasmic chaperone Skp in Escherichia coli". The experimental work was conducted at the Bacterial Cell Biology & Physiology group of the University of Amsterdam.

opencc-by-4.0Feb 2023View details →

ITC data set of DNA binding by YdaT repressor from Escherichia coli O157:H7

Raw isothermal titration calorimetry data set from the published article Prolic-Kalinsek, M., Volkov, A. N., Hadzi, S., Van Dyck, J., Bervoets, I., Charlier, D. & Loris, R. Structural basis of DNA binding by YdaT, a functional equivalent of the CII repressor in the cryptic prophage CP-933P from Escherichia coli O157:H7. (2023). Acta Cryst. D79, 245-258. DOI: 10.1107/S2059798323001249. Concentrations in the files are expressed as monomer protein and duplex DNA. Titrations were measured at 25 °C. Buffer is 10 mM NaH2PO4, 10 mM Na2HPO4, 100 mM NaCl, 50 mM glutamic acid, 50 mM arginine pH 7.5.

opencc-by-4.0Sep 2021View details →

ITC data set of nanobody (Nb33) binding to PaaR2 repressor truncates from Escherichia coli O157:H7

Raw isothermal titration calorimetry data set from the published article De Bruyn, P., Prolič-Kalin&scaron;ek, M., Vandervelde, A., Malfait, M., Sterckx, Y. G. J., Sobott, F., Hadži, S., Pardon, E., Steyaert, J., & Loris, R. (2021). Nanobody-aided crystallization of the transcription regulator PaaR2 from Escherichia coli O157:H7. Acta crystallographica. Section F, Structural biology communications, 77(Pt 10), 374–384. https://doi.org/10.1107/S2053230X21009006. Titrations were measured at different temperatures (5-37 °C, indicated in the file name). Concentrations are listed in each itc data file. Buffer is 10 mM NaH2PO4, 10 mM Na2HPO4, 150 mM NaCl, 0.01% Triton X-100, pH 7.5.

opencc-by-4.0Sep 2024View details →

Dataset for publication ("Bacterial microcompartment utilisation in the human commensal Escherichia coli Nissle 1917")

Experimental dataset used to create Figures 2-5 of manuscript: "Bacterial microcompartment utilisation in the human commensal Escherichia coli Nissle 1917".

opencc-by-4.0Oct 2024View details →

Dataset of the paper Antibacterial plant combinations prevent postweaning diarrhea in organically raised piglets challenged with enterotoxigenic Escherichia coli F18

Dataset of the paper: Antibacterial plant combinations prevent postweaning diarrhea in organically raised piglets challenged with enterotoxigenic Escherichia coli F18 meta_data: tracking information my_raw_data: raw data my_table: processed data

opencc-by-4.0Oct 2024View details →

Interactions between methylerythritol phosphate (MEP) pathway metabolites and Escherichia coli K-12 fatty acid biosynthesis enzymes

Raw data files from native mass spectrometry analyses examining the interactions between methylerythritol phosphate (MEP) pathway metabolites and Escherichia coli K-12 fatty acid biosynthesis enzymes. Recorded in positive mode direct injection.

opencc-zeroJul 2019View details →

Data from: Long-term evolution of the natural isolate of Escherichia coli 536 in the mouse gut colonized after maternal transmission reveals convergence in the constitutive expression of the lactose operon.

In vitro experimental evolution has taught us many lessons on the molecular bases of adaptation. To move towards more natural settings, evolution in the mice gut has been successfully performed. Yet, these experiments suffered from the use of laboratory strains as well as the use of axenic or streptomycin treated mice to maintain the inoculated strains. To circumvent these limitations, we conducted a one-year experimental evolution in vivo using a natural isolate of E. coli, strain 536, in conditions mimicking as much as possible natural environment with mother to offspring microbiota transmission. Mice were then distributed in 24 independent cages and separated in two different diets: a regular one (Chow diet, CD) and high-fat high-sugar one (Western diet, WD). Genome sequences revealed an early and rapid selection during the breast-feeding period that selected the constitutive expression of the well-characterized lactose operon. E. coli was lost significantly more in CD than WD, however, we could not detect any genomic signature of selection, nor any diet specificities during the later part of the experiments. The apparently neutral evolution presumably due to low population size maintained nevertheless at high frequency the early selected mutations affecting lactose regulation. The rapid loss of lactose operon regulation challenges the idea that plastic gene expression is both optimal and stable in the wild.

opencc-zeroAug 2021View details →

Data from: Uncovering key metabolic determinants of the drug interactions between trimethoprim and erythromycin in Escherichia coli

Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.

controlledIPD-YESFeb 2026View details →

A Clinical Trial to Evaluate a Recombinant Staphylococcus Aureus Vaccine (Escherichia Coli) in Healthy Adults

ClinicalTrials.gov study NCT03966040. IPD Sharing: YES. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

Prognostic Factors of Escherichia Coli Bloodstream Infections: Severity Score and Therapeutic Implications

ClinicalTrials.gov study NCT02890901. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

Shiga Toxin Producing Escherichia Coli (STEC) Volume Expansion

ClinicalTrials.gov study NCT03275792. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.