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102 results for “stationary phase”
Dataset for the evaluation of Supercritical Fluid Chromatography Polar Stationary Phases with OH moieties
<p>Raw data used for the evaluation of supercritical fluid chromatography stationary phases with OH moieties published in article "Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with OH moieties" in Analytical Chemistry, 2024. Data set contains: (i) chromatograms of 107 analytes measured on silica, hybrid silica, and diol column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet). </p>
DeepBacs – Escherichia coli release from stationary phase - Bright field segmentation dataset and StarDist model
<p>Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation.</p> <p>Additional information can be found on this<a href="https://github.com/HenriquesLab/DeepBacs/wiki"> github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli </em>cells of an overnight culture and the manually annotated segmentation mask.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field and segmented mask images </p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 2 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset)</p> <p>512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series)</p> <p><strong>Data annotation</strong>: Images were annotated using the Fiji freehand selection tool.</p> <p><strong>Image preprocessing</strong>: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped</p> <p><strong>StarDist model</strong></p> <p>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.</p> <p>Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578 </p>
Global effects of deletion of the sdh genes, encoding succinate dehydrogenase, and of cobalt on protein abundance in stationary phase Salmonella enterica serovar Typhimurium.
<p>A common strategy that bacteria utilize to increase their survival under stressful conditions in their natural environments, including antibiotic treatment, is the entry into quiescence, a state of reversible cell growth arrest that offers protection against many environmental insults. Understanding quiescence is an important fundamental question, with relevance in the medical and environmental fields. Little is known about the molecular and physiological determinants that orchestrate survival during this temporary arrest of proliferation, or those that allow a rapid transition back to the proliferating state when conditions again become favorable. In the wide host-range pathogen <em>Salmonella enterica</em> serovar Typhimurium (S. Typhimurium) and other Gram-negative bacteria, this temporary arrest of proliferation induces the expression of the alternative sigma subunit of RNA polymerase, σS/RpoS, which remodels global gene expression to reshape the cell physiology and ensure survival under starvation and various stress conditions (<em>i.e</em>., the general stress response). One important aspect of persistence is the phenotypic differentiation of quiescent populations into sub-population(s) of "persisters" that survive in the presence of lethal concentrations of antibiotics. This phenomenon is worsening the worldwide antibiotic crisis, by causing therapy failure and chronic infections and potentially favoring the development of antibiotic resistance. Understanding mechanisms governing bacterial persisters is thus an important topic and a key issue for drug developments. However, despites many studies, the physiological and molecular mechanisms controlling the formation of persisters are poorly understood and controversial.</p> <p>We have recently discovered an unexpected functional interaction between σS and succinate dehydrogenase (Sdh) in the formation of persisters. Succinate dehydrogenase (Sdh), a membrane bound complex that connects the TCA cycle and respiratory chain, is one major target down regulated by σS (Levi-Meyrueis <em>et al</em>. 2014, 2015, Lago <em>et al.</em> 2017). Stationary-phase <em>Salmonella</em> grown in LB rich medium form persisters with a higher frequency than actively growing bacteria, after transfer to fresh LB medium in the presence of lethal concentrations of ampicillin and ciprofloxacin, but not significant effect of the Δ<em>rpoS</em> mutation on this phenomenon was observed. Surprisingly however, the Δ<em>rpoS</em> mutation suppressed the defect in persister formation of a Δ<em>sdh </em>mutant. It is very likely that the Δ<em>rpoS</em> mutation compensates for a metabolic perturbation provoked by the Δ<em>sdh </em>mutation, and key for persister formation.</p> <p>To get further insights into the synthetic rescue process involved in persisters formation, we used a mass spectrometry-based proteomics approach to compare the proteome of the wild-type, Δ<em>rpoS</em>, Δ<em>sdh</em> and Δ<em>sdh</em>Δ<em>rpoS</em> strains grown to late stationary phase in nutrient-rich LB medium. Cells were also grown in LB supplemented with cobalt to pinpoint major changes induced by cobalt on the <em>Salmonella</em> proteome. Indeed, the synthetic rescue process involved in persisters formation in the presence of ampicillin was abolished when the inoculum has been grown in the presence of a non-lethal dose of cobalt (100 μM).</p> <p><strong>Accession number</strong>.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier <strong>PXD043726.</strong></p> <p><strong>See also:</strong></p> <p>NOREL, F., & MONTEIL, V. (2023). Unraveling a synthetic rescue process involved in persisters formation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10277562</p> <p>Phégnon, L., Uttenweiler-Joseph, S., & Létisse, F. (2024). <span>Key physiological and metabolic characteristics for the differentiation of quiescent Salmonella's cells into persisters [Data set]. </span>Zenodo. <a href="https://doi.org/10.5281/zenodo.10885905" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10885905</a></p> <p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p> <p><strong>References</strong></p> <p>Levi-Meyrueis C, Monteil V, Sismeiro O, Dillies MA, Monot M, Jagla B<em>, et al. </em>Expanding the RpoS/sigmaS-network by RNA sequencing and identification of sigmaS-controlled small RNAs in <em>Salmonella</em>. PloS one. 2014;9(5):e96918.</p> <p>Levi-Meyrueis C, Monteil V, Sismeiro O, Dillies M-A, Kolb A, Monot M <em>et al</em>. Repressor activity of the RpoS/sigmaS-dependent RNA polymerase requires DNA binding. Nucleic Acids Res 2015 43, 1456–1468.</p> <p>Lago M, Monteil V, Douche T, Guglielmini J, Criscuolo A, Maufrais C, <em>et al</em>. Proteome remodelling by the stress sigma factor RpoS/sigma(S) in <em>Salmonella</em>: identification of small proteins and evidence for post-transcriptional regulation. Scientific reports. 2017;7(1):2127.</p> <p> </p>
Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements
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Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB.
<p>The RpoS/σS sigma subunit of RNA polymerase is the master regulator of the general stress response in many Gram-negative bacteria.</p><p>We have shown that in <i>Salmonella enterica </i>serovar Typhimurium, RpoS activates transcription of the <i>sitABCD</i> and <i>mntH </i> genes, involved in iron and manganese transport, and that of <i>corA</i> encoding the main magnesium transporter (Levi-Meyrueis <i>et al</i>. 2014, Metaane <i>et al</i>. 2022, Metaane <i>et al. </i>2023). In addition, RpoS represses expression of the CbiO protein produced from the <i>cbiMNQO</i> operon encoding a high affinity cobalt uptake system (Lago <i>et al.</i> 2017, Metaane <i>et al</i>. 2022, Metaane <i>et al. </i>2023). Moreover, Inductively coupled plasma mass spectrometry analyses have revealed that the Δ<i>rpoS</i> mutation reduces the cellular concentration of manganese and magnesium and increases the concentration of cobalt of stationary phase <i>Salmonella </i>(Metaane <i>et al. </i>2022). These findings suggested that a tight control of uptake and availability of manganese, magnesium and cobalt might be critical for quiescent bacteria. Consistent with this hypothesis, our recent findings unraveled the importance of RpoS and magnesium in the regrowth potential of quiescent <i>Salmonella</i> cells (Metaane <i>et al</i>. 2022).</p><p>Unexpectedly, our recent work revealed that, under magnesium proficient environmental conditions, the absence of the housekeeping Mg2+ transporter CorA is sensed by the cell which induces compensatory mechanisms to minimize the impact of a Δ<i>corA</i> mutation on protein content, magnesium homeostasis, growth, and motility of <i>Salmonella </i>(Metaane <i>et al. </i>2023). In this study, we used a mass spectrometry-based proteomics approach to address the physiological impact of the SitABCD, MntH and CbiMNQO transporters on quiescent <i>Salmonella. </i>A comprehensive quantitative proteomic analysis was performed using wild-type and Δ<i>rpoS </i>strains of <i>Salmonella</i> ATCC14028 carrying deletions of these genes and grown to late stationary phase in nutrient-rich LB medium, <i>i.e</i>. the growth conditions previously used to characterize the RpoS- transcriptome, proteome and ionome (Levi-Meyrueis <i>et al.</i> 2014, Lago <i>et al. </i>2017, Metaane <i>et al. </i>2022). Since CorA can also import cobalt, a Δ<i>corA</i> mutation was included and combined with the Δ<i>cbiMNQO</i> mutation. </p><p><strong>Accession number</strong>.The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier <strong>PXD043760</strong>.</p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p><p><strong>References</strong></p><p>Levi-Meyrueis C, Monteil V, Sismeiro O, Dillies MA, Monot M, Jagla B, Coppée J-Y, Dupuy B, Norel F. Expanding the RpoS/sigmaS-network by RNA sequencing and identification of sigmaS-controlled small RNAs in <i>Salmonella</i>. PloS one. 2014;9(5):e96918.</p><p>Lago M, Monteil V, Douche T, Guglielmini J, Criscuolo A, Maufrais C, Matondo M, Norel F. Proteome remodelling by the stress sigma factor RpoS/sigma(S) in <i>Salmonella</i>: identification of small proteins and evidence for post-transcriptional regulation. Scientific reports. 2017;7(1):2127.</p><p>Metaane S, Monteil V, Ayrault S, Bordier L, Levi-Meyreuis C, Norel F. The stress sigma factor sigmaS/RpoS counteracts Fur repression of genes involved in iron and manganese metabolism and modulates the ionome of <i>Salmonella enterica </i>serovar Typhimurium. PLoS one. 2022;17(3):e0265511.</p><p>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one 2023;18(9):e0291736.</p><p>NOREL, & MONTEIL. (2023). Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS) (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8085835</p><p> </p><p> </p>
Data from: Phenotypic stochasticity prevents lytic bacteriophage population from extinction during bacterial stationary phase
It is generally thought that the adsorption rate of a bacteriophage correlates positively with fitness, but this view neglects that most phages rely only on exponentially growing bacteria for productive infections. Thus, phages must cope with the environmental stochasticity that is their hosts' physiological states. If lysogeny is one alternative, it is unclear how strictly lytic phages can survive the host stationary phase. Three scenarios may explain their maintenance: (1) pseudolysogeny, (2) diversified or (3) conservative bet-hedging. In order to better understand how a strictly lytic phage survives the stationary phase of its host, and how phage adsorption rate impacts this survival, we challenged two strictly lytic phage λ, differing in their adsorption rates, with stationary phase Escherichia coli cells. Our results showed that, pseudolysogeny was not responsible for phage survival and that, contrary to our expectation, high adsorption rate was not more detrimental during stationary phase than low adsorption rate. Interestingly, this last observation was due to the presence of the "residual fraction" (phages exhibiting extremely low adsorption rates), protecting phage populations from extinction. Whether this cryptic phenotypic variation is an adaptation (diversified bet-hedging) or merely reflecting unavoidable defects during protein synthesis remains an open question.
Data from: Phenotypic stochasticity prevents lytic bacteriophage population from extinction during bacterial stationary phase
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Comparison of S. aureus PVL-positive and PVL-negative isogenic strains in exponential and stationary phase of growth
GEO Series GSE6985. Staphylococcus aureus. 12 samples. Type: Expression profiling by array.
Comparison of gene expression profiles for heat-shocked and non-heat shocked stationary phase samples from heat tolerant isolates R1-0006 and R1-0007 and reference strain SL476
GEO Series GSE103418. Salmonella enterica subsp. enterica serovar Heidelberg. 18 samples. Type: Expression profiling by high throughput sequencing.
Gene expression of Vibrio parahaemolyticus in the early stationary phase
GEO Series GSE65448. Vibrio parahaemolyticus RIMD 2210633. 16 samples. Type: Expression profiling by array.
Transcriptomic profiles of S. Typhimurium SL1344 parent strain and ΔdksA and ΔrelAΔspoTΔdksA mutant strains grown to various optical densities during late-log and stationary phase in LB aerobic batch
GEO Series GSE63713. Salmonella enterica subsp. enterica serovar Typhimurium str. DT104; Salmonella enterica subsp. enterica serovar Typhimurium str. LT2; Salmonella enterica subsp. enterica serovar Enteritidis; Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91; Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344. 30 samples. Type: Expression profiling by array.
Stationary phase gene regulation in M1 GAS by the group A Streptococcus protein RocA
GEO Series GSE131235. Streptococcus pyogenes. 4 samples. Type: Expression profiling by high throughput sequencing.
Time-course gene expression profiles to understand compositional changes of the E. coli K-12 MG1655 transcriptiome during the transition from the exponential growth to the stationary phase
GEO Series GSE226643. Escherichia coli K-12. 36 samples. Type: Expression profiling by high throughput sequencing.
Acinetobacter oleivorans DR1 : WT stationary phase vs. aqsR mutant stationary phase
GEO Series GSE44347. Acinetobacter oleivorans DR1. 2 samples. Type: Expression profiling by high throughput sequencing.
Effect of Tup1, Xbp1, Isw2, and Sds3 on transcription in diauxic shift and stationary phase.
GEO Series GSE210930. Saccharomyces cerevisiae. 20 samples. Type: Expression profiling by high throughput sequencing.
Stationary phase gene regulation by the group A Streptococcus protein RocA
GEO Series GSE131239. Streptococcus pyogenes. 4 samples. Type: Expression profiling by high throughput sequencing.
Impact of the (p)ppGpp synthesis and maintenance of GTP homeostasis during stationary phase starvation on the transcriptome
GEO Series GSE254567. Staphylococcus aureus. 12 samples. Type: Expression profiling by high throughput sequencing.
Oxidative stress in stationary-phase cultures
GEO Series GSE3729. Saccharomyces cerevisiae. 219 samples. Type: Expression profiling by array.
RNA-seq of PAO1 and ΔlasR grown at 37°C or 25°C at stationary phase
GEO Series GSE304229. Pseudomonas aeruginosa. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis by total RNA sequencing of Cereibacter sphaeroides RNase E, RNase III or StsR mutant strains grown to stationary phase
GEO Series GSE271933. Cereibacter sphaeroides 2.4.1. 30 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.