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107 results for “Trypanosoma brucei brucei”
X-ray diffraction images for L-threonine dehydrogenase from Trypanosoma brucei with NAD and pyruvate bound.
<p>X-ray diffraction images which were collected at ESRF (Grenoble) using an ADSC 315r CCD detector on beamline ID29 on 11th November 2009. More details are given in the uploaded notes. </p>
Microscopy Imaging Dataset: Trypanosoma brucei Bloodstream Form Classification Using Deep Learning
<p>This dataset provides a comprehensive collection of microscopic images and associated labels, specifically designed to facilitate the automated classification of <em>Trypanosoma brucei</em> bloodstream forms—slender and stumpy. Accurate differentiation of these life cycle stages is vital for understanding the parasite's biology, transmission dynamics, and adaptation mechanisms in its mammalian host.</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Image Data</strong>: Microscopic images of <em>T. brucei</em> bloodstream forms captured under standard imaging conditions, encompassing a broad array of image quality, cellular arrangements, and morphological characteristics.</li> <li><strong>Label Data</strong>: Annotation files for each image, specifying cellular forms as slender or stumpy, essential for supervised machine learning applications.</li> <li><strong>Supplementary Files</strong>: Additional Excel files providing information on training, testing, and validation splits, alongside test results for model evaluation.</li> </ul> <p><strong>Purpose:</strong></p> <p>This dataset serves as a valuable resource for researchers in parasitology, machine learning, and computational biology. It supports investigations into the biology and life cycle of <em>T. brucei</em>, while also providing a robust testbed for developing, validating, and benchmarking image processing and classification algorithms tailored to parasite morphology.</p> <p><strong>Data Collection and Methodology:</strong></p> <p>The dataset was compiled using advanced deep learning techniques, integrating the Cellpose segmentation algorithm with a custom-trained Xception model optimized for classifying <em>T. brucei</em> forms. The model achieved 97% classification accuracy, demonstrating effective application in handling complex cell images and distinguishing between slender and stumpy forms in challenging imaging conditions.</p> <p><strong>Usage:</strong></p> <p>Researchers are encouraged to use this dataset to:</p> <ul> <li>Analyze and classify the life cycle stages of <em>T. brucei</em> bloodstream forms in microscopic images.</li> <li>Develop and test deep learning models for single-cell image segmentation and classification.</li> <li>Explore cellular morphology patterns and refine machine learning approaches for other single-cell imaging applications.</li> </ul> <p><strong>Citation:</strong></p> <p>Please cite the original dataset if you utilize this resource in your research to acknowledge its contribution to the field.</p> <p><strong>Access and Availability:</strong></p> <p>This dataset is openly available through Zenodo, enabling researchers to download, explore, and apply it in various fields, from parasitology to advanced computational biology.</p>
Fig. 2 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 2 Effect of lasalocid acid and salinomycin on the growth of bloodstream forms of T. brucei and human myeloid leukaemia HL-60 cell. Trypanosomes (circles) and HL-60 cells (squares) were incubated with varying concentration of lasalocid acid (closed symbols) or salinomycin (open symbols). After 72 h of culture, cell viability and proliferation were determined with the colorimetric dye resazurin. The experiment was repeated three times and mean values ± SD of three experiments are shown
Fig. 3 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 3 Effect of polyether ionophore antibiotics on the cell volume of bloodstream forms of T. brucei. a Trypanosomes (5 × 107 cell/ml) were incubated with 100 μM lasalocid acid (triangles) or salinomycin (squares) in Baltz medium in the presence of 1% DMSO. Controls (circles) were incubated with 1% DMSO. Every 10 min, the absorbance at 490 nm was measured. Mean values ± SD of three experiments are shown. Except for the time point 0 min at all other time points, the absorbance values were statistically significantly different from each other (One-way ANOVA test, p <0.01). b Trypanosomes (5 × 107 cell/ml) were incubated with 100 μM lasalocid acid in the absence (closed circles, solid line) or presence of 6 mM EDTA (open squares, dashed line) in Baltz medium containing 1% DMSO. Every 10 min, the absorbance at 490 nm was measured. Mean values of three experiments are shown. For clarity, the standard deviations were omitted. The standard deviations ranged between 17.5–25.1% of the mean values. At each time point, the data points of the two curves were statistically not significantly different (p = 0.465–0.977, Student's t test)
Fig. 1 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 1 Structure of lasalocid acid. The PubChem compound identifier (CID) for the compound is shown in parentheses
Fig. 2. ITS1 in Wild chimpanzees are infected by Trypanosoma brucei
Fig. 2. ITS1-based dendogram of trypanosomes from primate tissue and fecal samples. Sequences generated in this study are marked as follows: T ‾ tissue samples of apes (TA) and monkeys (TM); F ‾ fecal samples of apes (FA); sequences retrieved from GenBank are labeled with Latin names (Trypanosoma sp. ex Wildebeest JN673403, for T. theileri JX178185, HQ664848, and HQ664849).
Fig. 1. ITS1 in Wild chimpanzees are infected by Trypanosoma brucei
Fig. 1. ITS1-based detection of trypanosomes in blood and feces of experimentally infected mice. (A‾D, I) detection in blood; (E‾H, J) detection in feces. (A, E) Trypanosoma b. brucei; (B, F) T. b. gambiense; (C, G) T. b. rhodesiense; (D, H) T. b. evansi; (I) blood from a non-infected mouse; (J) feces from a non-infected mouse; (K) negative control; (m) marker.
Known and Predicted GPI-anchored proteins in Trypanosoma brucei
<p>This table is a list of proteins that are either known or predicted to be a GPI-anchored proteins in <em>Trypanosoma brucei</em>. </p>
Suppl. files to: Cellular and molecular targets of nucleotide-tagged trithiola-to-bridged arene ruthenium complexes in the protozoan para-sites Toxoplasma gondii and Trypanosoma brucei
<p>These are supplementary files for the manuscript entitled:</p> <p>Cellular and molecular targets of nucleotide-tagged trithiolato-bridged arene ruthenium complexes in the protozoan parasites <em>Toxoplasma gondii</em>and <em>Trypanosoma brucei</em></p> <p>submitted to International Journal of Molecular Sciences</p> <p>by: <strong>Nicoleta Anghel<sup>1¥</sup>, Joachim Müller<sup>1¥*</sup>, Mauro Serricchio<sup> 2</sup>, Jennifer Jelk <sup>2</sup>, Peter Bütikofer<sup>2</sup>, Ghalia Boubaker<sup>1</sup>, Dennis Imhof<sup>1</sup>, Jessica Ramseier<sup>1</sup>, Oksana Desiatkina<sup>3</sup>, Emilia Păunescu<sup>3</sup>, Sophie Braga-Lagache<sup>4</sup>, Manfred Heller<sup>4</sup>, Julien Furrer<sup>3</sup>, Andrew Hemphill<sup>1*</sup></strong></p>
Signatures of hybridization in Trypanosoma brucei
Open the record for dataset details and reuse information.
Trypanosoma brucei bloodstream form tagging: Targeted subcellular protein localisation.
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen Trypanosoma brucei by endogenous tagging with mNeonGreen (mNG). This master deposition includes a summary of the localisations, primer sequences and DOI indexing, provided in a directory structure analogous to the TrypTag genome-wide procyclic form project: <a href="https://doi.org/10.5281/zenodo.6862298">https://doi.org/10.5281/zenodo.6862298</a> It does not include any microscopy data, which are spread over multiple Zenodo DOIs. Instead, this deposition and the raw and processed data directories include an index to each DOI.</p> <p><strong>localisations.tsv</strong><br> Tab-delimited table, which can be opened in Excel, of localisation annotations for each gene tagged. Also includes primer sequences used, the 96 well plate in which tagging was carried out organised with one row per gene ID, with sets of columns for N and C terminal tagging.</p> <p><strong>geneselection.tsv</strong><br> Tab-delimited table of criteria used for gene selection for tagging. This includes presence/absence of a <em>Leishmania major </em>or <em>Trypanosoma cruzi </em>ortholog, localisation and signal intensity by procyclic form tagging (TrypTag) and upregulation at mRNA level.</p> <p><strong>id_doi_index.tsv</strong><br> Tab-delimited table listing all Trypanosoma brucei Lister 427 gene IDs, if tagging was attempted at the N or C terminus and, if so, the Zenodo DOI at which to find the microscopy data. To download data for a particular gene ID, find its entry in this table, go to the corresponding Zenodo DOI and download <plateid_date>.zip for the raw microscopy data or <plateid_date>_processed.zip for the processed microscopy data. In the latter, images are named by gene ID and tagged terminus.</p> <p><strong>plate_doi_index.tsv</strong><br> Tab-delimited table listing all 96 plates which were part of the targeted bloodstream form tagging project and the Zenodo DOI at which the data can be found. Downloading the data from all of these Zenodo DOIs gives the full microscopy dataset.</p> <p><strong>trypTag_BSF_master.zip</strong><br> Zip file containing the master directory structure for the targeted bloodstream form tagging project database. This contains all internal code which was used to build the bloodstream form tagging database from the raw microscopy data.</p> <p><strong>readme.docx</strong><br> Documentation on data access and rebuilding the database using trypTag_BSF_master.zip</p>
TrypTag: Genome-wide subcellular protein localisation in Trypanosoma brucei.
<p>TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). This master deposition includes a summary of the localisations, scripts, code and primer sequences used to build the TrypTag database, provided in the master directory structure. It does not include any microscopy data, which are spread over multiple Zenodo DOIs. Instead, this deposition and the raw and processed data directories include an index linking gene IDs to each Zenodo DOI. Data can also be browsed at <a href="http://tryptag.org/">TrypTag.org</a>.</p> <p>If you use this data resource please cite Billington <em>et al.</em> 2023 <em>Nature Microbiology </em>(<a href="https://doi.org/10.1038/s41564-022-01295-6">doi:10.1038/s41564-022-01295-6</a>). We recommend including this citation in the results or methods if TrypTag was used as part of a discovery process. If directly using TrypTag images, please also indicate in the figure legend or similar which images are from TrypTag. If carrying out a large-scale data analysis, please also cite this Zenodo deposition.</p> <p>Data can be mined via the cellular localization imaging or cellular component GO term searches at the genome database <a href="https://tritrypdb.org/">TriTrypDB.org</a> (part of <a href="https://veupathdb.org/">VEuPathDB</a>). If you do, please also <a href="https://tritrypdb.org/tritrypdb/app/static-content/about.html">cite</a> the genome database.</p> <p>You may also find the following papers informative: Dean <em>et al.</em> 2016 <em>Trends in Parasitology</em> (<a href="https://doi.org/10.1016/j.pt.2016.10.009">doi:10.1016/j.pt.2016.10.009</a>), which describes the original project aims and workflow. Halliday <em>et al.</em> 2019 <em>Molecular and Biochemical Parasitology</em> (<a href="https://doi.org/10.1016/j.molbiopara.2018.12.003">doi:10.1016/j.molbiopara.2018.12.003</a>), which describes the localisation ontology with example images and comparison to <em>Leishmania</em>.</p>
Trypanosoma brucei predicted protein structures, part 2 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 2 of 2. To get the full dataset, also download TbruceiTREU927_part1.zip from <a href="https://zenodo.org/record/7940748">doi:10.5281/zenodo.7940748</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p>
Trypanosoma brucei predicted protein structures, part 1 of 2
<p>AlphaFold2-predicted protein structures for the <em>Trypanosoma brucei</em> (TREU927) proteome, predicted using input multiple sequence alignments optimised for the Discoba lineage in which <em>T. brucei </em>sits. The structure prediction methodology was exactly as described in <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p> <p>This deposition contains part 1 of 2. To get the full dataset, also download TbruceiTREU927_part2.zip from <a href="http://zenodo.org/record/7948119">10.5281/zenodo.7948119</a>.</p> <p>Data are organised with one directory per <em>T. brucei </em>TREU927 gene ID (eg. Tb927.1.3600). Within each directory you will find:</p> <p><strong><gene id>_predmap.png</strong> A left to right representation of the linear protein sequence with one pixel per amino acid. Each horizontal bar represents one structure prediction, colour coded by pLDDT. For small proteins, there will likely be one prediction of the full-length protein. For large proteins, there may be many overlapping predictions.</p> <p><strong><gene id>_<start aa>-<end_aa> </strong>A directory containing structure prediction of that gene ID between the start and end amino acid. Within this directory you will find:</p> <p><strong><gene id>_<start aa>-<end_aa>.json</strong> The full data in a JSON format, including linear protein sequence and metadata, along with 5 predicted protein structures ranked from best to worst overall pAE. For each predicted protein structure, the structure (PDB format), its pLDDT per residue and pairwise pAE.</p> <p><strong><gene id>_<start aa>-<end_aa>_1.pdb</strong> The PDB file of the highest ranked structure.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-pae.png</strong> A plot of pAE, for the highest ranked structure, at one pixel per amino acid. Shade of green represents pAE for that amino acid pair, see below.</p> <p><strong><gene id>_<start aa>-<end_aa>_1-plddt.png</strong> A plot of pLDDT, for the highest ranked structure, at one horizontal pixel per amino acid. Bar height and colour both represent pLDDT for that amino acid, see below.</p> <p>PDB structure and pAE/pLDDT of lower ranked models are embedded in the JSON file.</p> <p>All pLDDT and pAE plots use the colour scales used by https://alphafold.ebi.ac.uk/: For pLDDT: > 90 (dark blue), 90 > pLDDT > 70 (light blue), 70 > pLDDT > 50 (orange), < 50 (yellow) discontinuous. For pAE: 0 (white angstrom) to dark green (32 angstrom) continuous.</p> <p>If you use this resource, please cite this Zenodo deposition and <a href="https://doi.org/10.1371/journal.pone.0259871">doi:10.1371/journal.pone.0259871</a>.</p>
Data from: Genetic diversity and population structure of Trypanosoma brucei in Uganda: implications for the epidemiology of sleeping sickness and Nagana
Background: While Human African Trypanosomiasis (HAT) is in decline on the continent of Africa, the disease still remains a major health problem in Uganda. There are recurrent sporadic outbreaks in the traditionally endemic areas in south-east Uganda, and continued spread to new unaffected areas in central Uganda. We evaluated the evolutionary dynamics underpinning the origin of new foci and the impact of host species on parasite genetic diversity in Uganda. We genotyped 269 Trypanosoma brucei isolates collected from different regions in Uganda and southwestern Kenya at 17 microsatellite loci, and checked for the presence of the SRA gene that confers human infectivity to T. b. rhodesiense. Results: Both Bayesian clustering methods and Discriminant Analysis of Principal Components partition Trypanosoma brucei isolates obtained from Uganda and southwestern Kenya into three distinct genetic clusters. Clusters 1 and 3 include isolates from central and southern Uganda, while cluster 2 contains mostly isolates from southwestern Kenya. These three clusters are not sorted by subspecies designation (T. b. brucei vs T. b. rhodesiense), host or date of collection. The analyses also show evidence of genetic admixture among the three genetic clusters and long-range dispersal, suggesting recent and possibly on-going gene flow between them. Conclusions: Our results show that the expansion of the disease to the new foci in central Uganda occurred from the northward spread of T. b. rhodesiense (Tbr). They also confirm the emergence of the human infective strains (Tbr) from non-infective T. b. brucei (Tbb) strains of different genetic backgrounds, and the importance of cattle as Tbr reservoir, as confounders that shape the epidemiology of sleeping sickness in the region.
Trypanosoma brucei L-threonine dehydrogenase diffraction images.
<p>Diffraction images for apo-L-threonine dehydrogenase from <em>T. brucei</em>. </p>
Trypanosoma brucei bloodstream form tagging: plate R7852 (replicate dated 20180328)
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate R7852, replicate dated 20180328.</p>
Trypanosoma brucei bloodstream form tagging: plate R7854 (replicate dated 20180403)
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate R1566, replicate dated 20180201.</p>
Trypanosoma brucei bloodstream form tagging: plate R7856 (replicate dated 20180612)
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate R7856, replicate dated 20180612.</p>
Trypanosoma brucei bloodstream form tagging: plate R1566 (replicate dated 20180201)
<p>Trypanosoma brucei bloodstream form tagging protein localisation data. Widefield epifluorescence microscope images of protein subcellular localisation in the bloodstream form life cycle stage of the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate R1566, replicate dated 20180201.</p>
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