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opencc-by-4.0Oct 2021View details →

DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model

Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model. Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>. The example shows a bright field image of live E. coli cells and the respective annotation for specific growth stages.   Training and test dataset Data type: Paired microscopy images (bright field) and annotations in PASCAL VOC format Microscopy data type: 2D bright field images recorded at 1 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .png (8-bit) Image size: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) Image preprocessing: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using LabelImg (https://github.com/tzutalin/labelImg).   YOLOv2 model The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.   Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de   Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578

opencc-by-4.0Oct 2021View details →

DeepBacs – Escherichia coli bright field segmentation dataset

Training and test images of live E. coli cells imaged under bright field for the task of segmentation. Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>. The example shows a bright field image of live E. coli cells and the manually annotated segmentation mask.   Data type: Paired bright field and segmented mask images  Microscopy data type: 2D bright field images recorded at 1 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .tif (8-bit) Image size: 1024 x 1024 px² (79 nm / pixel), 19/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) Image preprocessing: Raw images were recorded in 16-bit mode (image size 512 x 512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. Two sets of mask images are provided: RoiMaps for instance segmentation using e.g. StarDist or binary images for CARE or U-Net. Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de   Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578

opencc-by-4.0Oct 2019View details →

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network” by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou  and Erik van Nimwegen. A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>.  The repository consists of the following datasets:  1. preprocessed_datasets.zip(~22GB) <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>.  Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file directory_structure_in_preprocessed contains information about how the files are organized.</li> </ul> 2. info_files: This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information.  3. processed_datasets: These files correspond to the processed datasets from the raw Rdata files under 1 above.  The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded  flow cytometry measurements from promoter/growth-condition combinations that  contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper: <ul> <li>FULL_dataset_mean_var_wreplicates: In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all  promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li>dataset_with_noise_estimates: In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor,  in each of the conditions. </li> <li>time_course_data_SI: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li>growth_curves_SI: Growth data (OD600 as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li>singlecell_areas_SI: Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li>synthetic_promoters_dataset: This dataset contains mean, variance and noise measurements of a set of constitutive promoters from  <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li>MARA_results:  All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>

opencc-by-4.0Nov 2021View details →

Salmonella, Shiga toxin-producing Escherichia coli O157:H7 and Listeria monocytogenes numbers during dry-aging of beef loins

This dataset contains bacterial count data and loin characteristics from an experimental study assessing the survival/growth of Salmonella, Escherichia coli O157:H7 and Listeria monocytogenes during dry-aging of beef loins, after artificial inoculation.  Four different csv files are provided with tabular data. A detailed description of the data is provided in the readme file.

opencc-by-4.0Mar 2022View details →

DeepBacs – Escherichia coli release from stationary phase - Bright field segmentation dataset and StarDist model

Training and test images of live E. coli cells imaged under bright field for the task of segmentation. Additional information can be found on this<a href="https://github.com/HenriquesLab/DeepBacs/wiki"> github wiki</a>. The example shows a bright field image of live E. coli cells of an overnight culture and the manually annotated segmentation mask.   Data type: Paired bright field and segmented mask images  Microscopy data type: 2D bright field images recorded at 2 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .tif (8-bit) Image size: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset) 512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series) Data annotation: Images were annotated using the Fiji freehand selection tool. Image preprocessing: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped StarDist model The StarDist 2D model was trained from scratch for 200 epochs on 33 paired image patches (image dimensions: (512, 512 px²), patch size: (512 x 512 px²)) with a batch size of 2, 80 rays, grid size 1, 4-fold data augmentation and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.13) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU. Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin. Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de Affiliation(s):  1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263  3) ORCID: 0000-0002-9821-3578

opencc-by-4.0Feb 2022View details →

Escherichia coli 13k assemblies PopPUNK database

# _Escherichia coli_ 13k reference ## PopPUNK database files ## v1.0.0 (1 March 2022) ### Description This tarball contains the PopPUNK v2.4.0 [1] database files of a clustering for the 13435 _E. coli_ assemblies from three studies [2-4]. A file matching the clustering with the multilocus sequence types [5] (identified using mlst v2.19.0 [6]) is provided in `ecoli_sequence_information.tsv`. The corresponding assemblies and a Themisto v2.1.0 [7] pseudoalignment index are also available as separate uploads in Zenodo. __Note:__ the `esc_ra9772aa_as` entry in the PopPUNK files does not have a corresponding assembly nor is it included in the Themisto pseudoalignment index or the `ecoli_sequence_information.tsv` file. This is because the entry for this sequence was corrupted in the original run of PopPUNK. ### Files - `pop_db`: the PopPUNK sketch files. - `pop_fit_dbscan`: the initial DBSCAN fit for the sketch. - `pop_fit_refined`: the final refined version of the DBSCAN fit. - `pop_fit_refined_viz`: microreact visualisation files from the refined fit. - `ecoli_sequence_information.tsv`: a tab-separated text file containing the MLST types and the PopPUNK clusters. ### References - [1] Lees J et al., _Fast and flexible bacterial genomic epidemiology with PopPUNK._ https://doi.org/10.1101/gr.241455.118 - [2] Horesh G et al., _A comprehensive and high-quality collection of Escherichia coli genomes and their genes._ https://doi.org/10.1099/mgen.0.000499 - [3] Gladstone R et al., _Emergence and dissemination of antimicrobial resistance in Escherichia coli causing bloodstream infections in Norway in 2002–17: a nationwide, longitudinal, microbial population genomic study._ https://doi.org/10.1016/S2666-5247(21)00031-8 - [4] Shao Y et al., _Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth._ https://doi.org/10.1038/s41586-019-1560-1 - [5] Jolley K et al., _Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications._ https://doi.org/10.12688/wellcomeopenres.14826.1 - [6] Seemann T, _mlst_ _GitHub._ https://github.com/tseemann/mlst - [7] Mäklin T et al., _Bacterial genomic epidemiology with mixed samples._ https://doi.org/10.1099/mgen.0.000691

opencc-by-4.0Jun 2022View details →

EFSA cgMLST gene lists for Escherichia coli and Salmonella enterica chewieNS schema

Annex A contains the list of cgMLST loci of Escherichia coli and Salmonella enterica used in the cgMLST analysis in the EFSA One Health WGS System.

opencc-by-4.0Jul 2022View details →

Code and Additional Files for the Manuscript "The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand"

These scripts were used in the"The Impact of Farming Practices on Resistance to Critically Important Antimicrobials in ESBL or AmpC-producing Escherichia coli in Thailand" manuscript. The scripts are ordered for ease of use. It also contains intermediate files and files necessary for the mapping. Table S1 containing the metadata is available in the supplementary information of the manuscript.

opencc-by-4.0Jul 2022View details →

Code and Data associated with "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with Escherichia coli"

Code and data sufficient to reproduce analyses in "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with Escherichia coli".

opencc-by-4.0Jul 2022View details →

Supplementary Materials of Bacillus subtilis Protects the Ducks from Oxidative Stress Induced by Escherichia coli: Efficacy and Molecular Mechanism

Figure S1: The KEGG classification of DEGs; Table S1: Analysis composition of basal diets and nutrient level (air-dry basis, %); Table S2: Primers used for the RT-qPCR in this study.

Fig. 4 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens

Fig. 4 — Percentage viability of HeLa cells with different mangrove extracts. Values are mean of triplicate reading (mean±SD)

Fig. 3 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens

Fig. 3 — SEM images of mangrove extracts with treated and un-treated bacterial cells: (a) Un-treated; (b) MTCC 435 treated with R. mucronata acetone stilt root extract; (c) MTCC 435 treated with B. cylindrica acetone leaf extract; (d) Un-treated Escherichia coli; (e) Escherichia coli treated with R. mucronata acetone stilt root extract; (f) Escherichia coli treated with B. cylindrica ethyl acetate leaf extract; (g) Un-treated Vibrio cholera; and (h) Vibrio cholerae treated with R. mucronata acetone stilt root extract

Fig. 2 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens

Fig. 2 — Viable count analysis of the bacterial pathogens - I: Collected solvent extract; II: Control plates [a) MTCC 435, g) E. coli, m) V. cholerae; b, h & n: Tetracycline (40 µg.ml-1) treated plates - (b) MTCC 435, h) E. coli, and n) V. cholerae); c, e, i, k & o: X- MBC concentration treated plates - (c) MTCC 435 with R. mucronata acetone stilt root extract, e) MTCC 435 with B. cylindrica acetone leaf extract, i) E. coli with R. mucronata acetone stilt root extract, k) E. coli with B. cylindrica acetone leaf extract, and o) V. cholerae with B. cylindrica ethyl acetate leaf extract); d, f, j, l & p: 2X-MBC concentration treated plates - (d) MTCC 435 with R. mucronata acetone stilt root extract, f) MTCC 435 with B. cylindrica acetone leaf extract, j) E. coli with R. mucronata acetone stilt root extract, l) E. coli with B. cylindrica acetone leaf extract, and p) V. cholerae with B. cylindrica ethyl acetate leaf extract)]

Fig. 1 in Bactericidal properties of mangrove Bruguiera cylindrica (L.) Blume leaf and Rhizophora mucronata Poir. stilt root extracts on Vibrio cholera, MTCC 435 and Escherichia coli pathogens

Fig. 1 — Effect of various extracts on growth pattern of clinical pathogens: a) R. mucronata acetone stilt root extract with MTCC 435; b) B. cylindrica acetone leaf extract with MTCC 435; c) R. mucronata acetone stilt root extract with E. coli; d) B. cylindrica acetone leaf extract with E. coli; and e) B. cylindrica ethyl acetate leaf extract with V. cholera

opencc-by-4.0Nov 2023View details →

Escherichia coli lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check

<h2>Escherichia coli lineage deconvolution indexes for Themisto, mSWEEP/mGEMS, and demix_check</h2> This dataset contains the indexes used in a study conducted in Punjab, Pakistan that investigated E. coli colonisation diversity in healthy carriage with the use of CLED enrichment plates. The following files are included in the download: <ul> <li>Themisto v3 pseudoalignment index.</li> <li>PopPUNK database.</li> <li>Demix_check index.</li> <li>Raw assembly data.</li> </ul> <h3>About</h3> <h4>Version history</h4> v0.1.1 (current version) <ul> <li>Added reference to the study.</li> </ul> v0.1.0 <ul> <li>Added brief description with a few missing parts.</li> </ul> <h4>Distribution</h4> These files are made available under a CC-BY 4.0 license. If you use these assemblies in your study please cite the source as appropriate (study will be added in a later version). <h4>Citation</h4> Khawaja, T., Mäklin, T., Kallonen, T. et al. Deep sequencing of _Escherichia coli_ exposes colonisation diversity and impact of antibiotics in Punjab, Pakistan. Nature Communications 15, 5196 (2024). <a href="https://doi.org/10.1038/s41467-024-49591-5">https://doi.org/10.1038/s41467-024-49591-5</a> <h3>Methods briefly</h3> <h4>Data</h4> Assembly data included originates from the following studies: <ul> <li>Horesh G, et al., A comprehensive and high-quality collection of Escherichia coli genomes and their genes. Microbial Genomics 2021. doi: <a href="https://doi.org/10.1099/mgen.0.000499">10.1099/mgen.0.000499</a></li> <li>Gladstone R, et al., Emergence and dissemination of antimicrobial resistance in Escherichia coli causing bloodstream infections in Norway in 2002–17: a nationwide, longitudinal, microbial population genomic study. The Lancet Microbe 2021. doi: <a href="https://doi.org/10.1016/S2666-5247(21)00031-8">10.1016/S2666-5247(21)00031-8</a></li> <li>Shao Y, et al., Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature 2019. <a href="https://doi.org/10.1038/s41586-019-1560-1">10.1038/s41586-019-1560-1</a></li> <li>Snaith AE, et al. The highly diverse plasmid population found in Escherichia coli colonizing travellers to Laos and its role in antimicrobial resistance gene carriage. Microbial Genomics 2023. doi: <a href="https://doi.org/10.1099/mgen.0.001000">10.1099/mgen.0.001000</a></li> <li>Habib A, et al. Dissemination of carbapenemase-producing Enterobacterales in the community of Rawalpindi, Pakistan. PLOS ONE 2022. doi: <a href="https://doi.org/10.1371/journal.pone.0270707">10.1371/journal.pone.0270707</a></li> <li>Runcharoen C, et al. Whole genome sequencing of ESBL-producing Escherichia coli isolated from patients, farm waste and canals in Thailand. Genome Medicine 2017. doi: <a href="https://doi.org/10.1186/s13073-017-0471-8">10.1186/s13073-017-0471-8</a></li> <li>Musicha P, et al. Trends in antimicrobial resistance in bloodstream infection isolates at a large urban hospital in Malawi (1998–2016): a surveillance study. The Lancet Infectious Diseases 2017. doi: <a href="https://doi.org/10.1016/S1473-3099(17)30394-8">10.1016/S1473-3099(17)30394-8</a></li> </ul> <h4>Themisto index construction</h4> Assemblies were indexed with <a href="https://github.com/algbio/themisto">Themisto</a> v3.0.0-rc using k-mer size 31 and the `--file-colors` option. <h4>PopPUNK clustering</h4> We followed the approach described in <a href="https://doi.org/10.1038/s41467-022-35178-5">Mäklin et al. 2022</a> using <a href="https://github.com/bacpop/poppunk">PopPUNK</a> v2.5.0. <h4>Demix check indexing</h4> The index was generated using the `setup_reference.sh` script from <a href="https://github.com/tmaklin/coreutils_demix_check">tmaklin/coreutils_demix_check</a>. <h3>Contact</h3> Tommi Mäklin <tommi'at'maklin.fi>.

Figure 5 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465

Figure 5. The interaction of amylopectin (PubChem ID - 439207) and alpha-amylase enzyme active site. The left figure is built by Chimera and the right by MOE software.

Figure 4 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465

Figure 4. Ramachandran plot for validation of alpha-amylase enzyme 3D structure generated by Raptor-X server.

Figure 3. 3 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465

Figure 3. 3-dimentionsl protein structure of alpha-amylase built by Raptor-X software.α-helices are indicated by red, β-sheet by yellow and random coils by white. The protein structure consists of a single monomer.

Figure 2 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465

Figure 2. Enzyme kinetics. (A) The effect of temperature on the enzyme stability; (B) determination of KM and Vmax values of purified alpha-amylase using Lineweaver-Burk plot; (C) The effect of reaction mixture temperature on the enzyme activity indicating an optimum temperature of 70°C; (D) The effect of pH on the enzyme activity, showing maximum activity at pH 8.

Figure 1 in Escherichia coli expression and characterization of -amylase from Geobacillus thermodenitrificans DSM-465

Figure 1. SDS-PAGE photograph indicating the expression and purification of alpha amylase. Lane C- control experiment (without gene), Lane-M. Protein marker (ThermoFisher Scientific PageRulerTM Prestained Protein Ladder, 10 kDa to 180 kDa), Lane E, experimental with expression of gene, Lane P, partially purified enzyme alpha amylase. The molecular weight of the purified enzyme was found as 63 kDa on SDS-PAGE.