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814 results for “LPS”
Murine Bone Marrow Derived Macrophages (BMDM's) stimulated with LPS and treated with PBS, Epirubicin and Aclarubicin
<p>This dataset was used in the analysis which composes the GitHub repository:</p> <p><a href="https://github.com/andrebolerbarros/Chora_etal_2022">https://github.com/andrebolerbarros/Chora_etal_2022</a></p> <p>The files presented here correspond to:</p> <p><em>gene_counts.tab:</em> the table for the gene counts produced by the alignment of fastq files using STAR;</p> <p><em>sampleTable.csv:</em> the treatment information for each sample produced.</p>
Sparktope example LPs
<p>This dataset consists of the main examples from the Sparktope compiler. These examples are pre-compiled and ready to pass to a solver. The filename format is <em>polytope</em>-<em>objective</em>.lp.gz where <em>polytope</em> is compiled from a .spk program and <em>objective</em> is compiled from a .in input description. These are the (relatively) easy versions of the examples with some extra constraints forcing the input variables. More challenging instances (that should still have the same optimal solutions) can be produced with e.g. "grep -v force <em>forced</em>.lp > <em>unforced</em>.lp"</p>
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis<br> K&REC_LPS_PEG_NaPA_K.docx <- File with ANOVA result of partition coefficient (K) for GFP<br> K&REC_LPS_PEG_NaPA_REC.docx <- File with ANOVA result of recover (REC) for GFP <br> K_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays <br> K_GFP_Pol_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays <br> K_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays <br> K_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays <br> K_GFP_Pol_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays <br> K_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays <br> REC_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays <br> REC_GFP_Pol_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays <br> REC_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays <br> REC_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays <br> REC_GFP_Pol_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays <br> REC_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays <br> REM_LPS_PEG_NaPA.docx <- File with ANOVA result of LPS removal <br> REM_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis<br> Stability_GFP_PEG_NaPA.docx <- File with ANOVA result of GFP stability<br> Stability_GFP_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP stability results for ANOVA analysis</p> <p>REM_LPS_Pol_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays <br> REM_LPS_Pol_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays <br> REM_LPS_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.05M salt assays <br> REM_LPS_Pol_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays <br> REM_LPS_Pol_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays <br> REM_LPS_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>K_GFP_Pol_025_NaCl_Li2SO4.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays comparing NaCl and Li2SO4 effect <br> K_GFP_Pol_025_NaCl_Li2SO4.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays comparing NaCl and Li2SO4 effect <br> K_GFP_Pol_025_NaCl_Li2SO4_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays comparing NaCl and Li2SO4 effect </p> <p>REM_LPS_Pol_KI_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KI assays comparing salt concentration effect <br> REM_LPS_Pol_KI_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KI assays comparing salt concentration effect<br> REM_LPS_Pol_KI_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KI assays comparing salt concentration effect<br> REM_LPS_Pol_KNO3_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KNO3 assays comparing salt concentration effect <br> REM_LPS_Pol_KNO3_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KNO3 assays comparing salt concentration effect<br> REM_LPS_Pol_KNO3_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KNO3 assays comparing salt concentration effect<br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in Li2SO4 assays comparing salt concentration effect <br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in Li2SO4 assays comparing salt concentration effect<br> REM_LPS_Pol_Li2SO4_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in Li2SO4 assays comparing salt concentration effect<br> REM_LPS_Pol_NaCl_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in NaCl assays comparing salt concentration effect <br> REM_LPS_Pol_NaCl_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in NaCl assays comparing salt concentration effect <br> REM_LPS_Pol_NaCl_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in NaCl assays comparing salt concentration effect</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis</p> <p>K_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (K) for GFP</p> <p>REC_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (REC) for GFP</p> <p>REM_LPS_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis</p> <p>REM_LPS_ORG_ANOVA.docx <- File with ANOVA result of removal of LPS</p> <p>Stability__ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP stability for ANOVA analysis</p> <p>Stability__ORG_ANOVA.docx <- File with ANOVA result of GFP stability</p> <p>K_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays </p> <p>K_ORG_glm_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays </p> <p>K_ORG_glm_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays </p> <p>K_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays </p> <p>REC_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays</p> <p>REC_ORG_glm_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays</p> <p>REC_ORG_glm_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REC_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REM_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays</p> <p>REM_ORG_glm_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays </p> <p>REM_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>REM_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays</p> <p>REM_ORG_glm_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays </p> <p>REM_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Monsoon low-pressure system (LPS) tracks in ERA5 over India (1979-2019) with added environmental variables
<p>Derived from the LPS v3.0 dataset (https://doi.org/10.5281/zenodo.7568990). Filtered to monsoon LPSs (majority of track lifetime between June and September), with genesis over the Bay of Bengal and making landfall over India. Temporal resolution also reduced from hourly to six-hourly. This dataset accompanies the paper "Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems" (DOI to follow).</p> <p>Aside from the core variables described in the main LPS dataset (linked above), this version includes a large number of environmental variables, listed below. All are computed from ERA5 unless otherwise stated, "<em>mean</em>" means that the variable is computed as an average within 400 km of the LPS centre, "<em>mcz</em>" means that the variable is computed as an average in the box [75-85°E, 18.5-27°N].<br> <em>mean_u200</em>: 200 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_u850</em>: 850 hPa zonal wind (m s<sup>-1</sup>)<br> <em>mean_skt</em>: surface temperature (K)<br> <em>mean_land_frac</em>: fraction of area within 400 km that is over land<br> <em>mcz_tcwv</em>: mean total column water vapour over monsoon trough (kg m<sup>-2</sup>)<br> <em>vortex_depth</em>: mean_vort_500 x mean_vort_700/mean_vort_850<sup>2</sup><br> <em>over_land</em>: flag for LPS centre (Boolean)<br> <em>dvo850_dt</em>: rate of change of mean_vort_850 (10<sup>-5</sup> s<sup>-1</sup> day<sup>-1</sup>) <br> <em>acc_land_time</em>: accumulated time where over_land = True (hours)<br> <em>total_land_time</em>: final value of acc_land_time} for a given LPS (hours)<br> <em>qshear_850</em>: meridional shear of 850 hPa specific humidity over India (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850</em>: meridional shear of 850 hPa zonal wind over India (m s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_cape</em>: CAPE (J kg<sup>-1</sup>)<br> <em>mcz_cape</em>: mean CAPE over the monsoon trough (J kg<sup>-1</sup>)<br> <em>mean_dthetae_dp_900_750</em>: d(theta_e)/dp between 900 and 750 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_dthetae_dp_750_500</em>: d(theta_e)/dp between 750 and 500 hPa, a measurement of atmospheric stability (K hPa<sup>-1</sup>)<br> <em>mean_land_skt</em>: land surface temperature (K; NaN over ocean)<br> <em>mean_sst</em>: sea surface temperature (K; NaN over land)<br> <em>mean_swvl1</em>: soil moisture in the top layer (m<sup>3</sup> m<sup>-3</sup>; <7 cm; NaN over ocean)<br> <em>mean_swvl2</em>: soil moisture in the second layer (m<sup>3</sup> m<sup>-3</sup>; 7-28 cm; NaN over ocean)<br> <em>mean_swvl1_grad</em>: mean absolute horizontal gradient of mean_swvl1 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>mean_swvl2_grad</em>: mean absolute horizontal gradient of mean_swvl2 (m<sup>3</sup> m<sup>-4</sup>)<br> <em>olr_90</em>: 90th percentile of negative OLR (i.e. ~90th percentile of cloud top height) (W m<sup>-2</sup>)<br> <em>olr_75</em>: 75th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>olr_50</em>: 50th percentile of negative OLR (W m<sup>-2</sup>)<br> <em>qshear_850_background</em>: qshear\_850 averaged over the previous ten days (m<sup>3</sup> m<sup>-3</sup> (°)<sup>-1</sup>)<br> <em>ushear_850_background</em>: ushear\_850 averaged over the previous ten days (m<sup>3</sup> s<sup>-1</sup> (°)<sup>-1</sup>)<br> <em>mean_q_850</em>: 850 hPa specific humidity (m<sup>3</sup> m<sup>-3</sup>)<br> <em>orography_height</em>: elevation of land surface under LPS centre (m)<br> <em>peak_vorticity</em>: largest value of mean_vort_850} attained by a given LPS (10<sup>-5</sup> s<sup>-1</sup>)<br> <em>reached_peak</em>: False if peak\_vorticity has not been reached yet, else True<br> <em>mean_prcp_400</em>: mean precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_prcp_800</em>: mean precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_400</em>: maximum precipitation rate within 400 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>max_prcp_800</em>: maximum precipitation rate within 800 km of the LPS centre over the next six hours (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_vimfd_400</em>: vertically integrated moisture flux convergence (kg m<sup>-2</sup> s<sup>-1</sup>)<br> <em>mean_v200</em>: 200 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v500</em>: 500 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_v850</em>: 850 hPa meridional wind speed (m s<sup>-1</sup>)<br> <em>mean_u500</em>: 500 hPa zonal wind speed (m s<sup>-1</sup>)<br> <em>zonal_speed</em>: zonal (x) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>merid_speed</em>: meridional (y) component of LPS propagation velocity (m s<sup>-1</sup>)<br> <em>mean_prcp_imerg</em>: as mean_prcp_400 but computed using IMERG data, rather than ERA5 (kg m<sup>-2</sup> hr<sup>-1</sup>)</p> <p> </p> <p>qshear_850, ushear_850 and their backgrounds are averaged over 5° longitude either side of the LPS centre, with the gradient computed between 10°N and 27°N, reflecting the moisture and zonal wind gradients across the monsoon region.</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015).</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
LPS's over Bay of Bengal and TC's over West North Pacific (WNP) simulated in the CESM 1.2 WNP SST warming experiment
<p>This repository contains the dataset as explained below:</p> <p>1) This dataset contains low-pressure systems (LPS) tracks over the Bay of Bengal (BoB) and West North Pacific (WNP) tropical cyclones (TCs) simulated in CESM 1.2 WNP SST warming experiment conducted by Srujan and Sandeep (2023) submitted to Nature Geosciences. The LPS and TCs are tracked from mean sea level pressure (MSLP) using the algorithm developed by Praveen et al. (2015). The file names start with "<strong>cyc_</strong>". This will be useful for reproducing Fig. 1 and Fig. A2</p> <p>2) The data helps reproduce the Heating rate due to the moist process (Fig. 2) starts with "<strong>DT_</strong>"</p> <p>3) The data helps reproduce the propagation of rossbywave (Fig. 3) starting with "<strong>regcof_</strong>"</p> <p>4) The data helps reproduce the input SST pattern (Fig. A1) starting with "<strong>sst_</strong>"</p> <p>5) The data helps reproduce the Hovmuller plot (Fig. A3) starting with "<strong>comp_</strong>"</p> <p>6) The data helps reproduce the lag1 mslp anomaly (Fig. A4) starting with "<strong>slpcomp_</strong>"</p> <p><strong>References:</strong></p> <p>Praveen, V., Sandeep, S., & Ajayamohan, R. S. (2015). <strong>On the relationship between mean monsoon precipitation and low pressure systems in climate model simulations</strong>. <em>Journal of Climate</em>, <em>28</em>(13), 5305-5324.</p>
Bulk RNAseq of chronic LPS treated female mouse pituitary across doses
<p>To investigate the mechanisms of chronic inflammation on gonadotropin secretion we performed bulk RNA-seq on pituitaries from female mice chronically exposed to lipopolysaccharide for 6 weeks.</p>
E. coli-ΦX174 genotype to phenotype map reveals flexibility and diversity in LPS structures
<p>Here are the raw sequencing data of all <em>E. coli</em> C and ΦX174 strains obtained during this study. A short description of each file's content can be found below.</p> <p> </p> <p><em>Bacteria</em></p> <ul> <li>"E.coliC_res.zip": Whole genome sequencing results of <em>E. coli</em> C WT and ΦX174-resistant strains (generated by fluctuation experiments). All<em> E. coli</em> C samples were prepared for whole-genome sequencing from one millilitre of stationary-phase cultures. Genomic DNA was extracted using the Wizard® Genomic DNA purification Kit (Promega, Germany). Bacterial samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany) using an Illumina Nextera DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</li> </ul> <p> </p> <ul> <li>"E.coliC_res_excluded.zip": Whole genome sequencing results of the four ΦX174-resistant <em>E. coli</em> C strains (generated by fluctuation experiments) that have been excluded from the analyses<em>: E. coli </em>C R1, R3, R15, and R19. We found that both<em> E. coli</em> C R3 and R15 were not isogenic. While whole-genome sequencing from their respective glycerol stocks showed only a single mutation in <em>galE</em>, whole-genome sequencing carried on colony re-streaks showed that additional mutations were systematically associated with the single mutation in <em>galE</em> (<strong>see file "E.coliC_res_excluded_10_clones</strong>). <em>E. coli</em> C R1 displayed an unstable resistant phenotype. Finally, <em>E. coli</em> C R19 was partially resistant to ΦX174 WT. It carries a single mutation in the <em>yajC</em> gene, which encodes for a periplasmic protein. No apparent link to the LPS biosynthesis or assembly has been discovered yet, but <em>yajC</em> might play a role in phage DNA injection into the bacterium’s cytoplasmic membrane.</li> </ul> <p> </p> <ul> <li>"E.coliC_res_excluded_10_clones.zip": Whole genome sequencing results of 10 randomly chosen isolates of<em> E. coli </em>C R1, R3, and R15.</li> </ul> <p> </p> <ul> <li>"EcoliC.gb": <em>E. coli</em> C WT strain used as reference.</li> </ul> <p> </p> <p><em>Bacteriophages</em></p> <p> </p> <ul> <li>“PhiX174_PCR_399r_400f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul> <p> </p> <p>PhiX174_399_r: CTTGACTCATGATTTCTTACC</p> <p>PhiX174_400_f: TTACTGAACAATCCGTACGTTTC</p> <p> </p> <p>DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</p> <p> </p> <ul> <li>“PhiX174_PCR_2361r_2362f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul> <p> </p> <p>PhiX174_2361_r: TCGCTTGGTCAACCCCTCAG</p> <p>PhiX174_2362_f: AGCGCGGTAGGTTTTCTGCT</p> <p> </p> <p>DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</p> <ul> <li>“PhiX174_excluded.zip”: Since we removed R19 from the final analysis, we also removed its corresponding evolved phage obtained during the first evolution experiment (ΦX174 R19 T1). We also removed the phage infecting R5 (ΦX174 R5 T2) because it was not isogenic (confirmed by Sanger Sequencing).</li> </ul> <ul> <li>“PhiX174_Sequencing_Sanger.zip”: Both <em>F</em> and <em>H</em> genes were amplified by performing 35 cycles of PCR using Phusion® High-Fidelity PCR Master Mix with HF Buffer. The sequencing primers are listed in the<strong> Information_Sanger_Samples_ID.xlsx</strong> file.</li> </ul> <p> </p> <ul> <li>“PhiX174_ref.gb”: PhiX174 WT strain used as reference.</li> </ul> <p> </p> <p>We also include the raw data and pictures from our spotting tests and plaque assays used to complete the final matrix of infection.</p> <ul> <li>Spotting_tests_reanalyzed.xlsx”: Raw data from the pictures (see <strong>Photos_matrices</strong><strong>.zip</strong>) used to generate the Hierarchical agglomerative clustering analysis of the host ranges of evolved phage and Infection matrix of evolved ΦX174 phages on the 31 resistant E. coli C strains.</li> </ul> <p>The <strong>Photos_matrices</strong><strong>.zip</strong> folder contains</p> <p> </p> <ul> <li>“SSA_matrix_Bact_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted phages on bacterial lawns.</li> </ul> <p> </p> <ul> <li>“SSA_matrix_Phi_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted bacteria on phage lawns.</li> </ul> <p> </p> <ul> <li>“Mismatches”: We performed plaque assays when combinations of phages and bacteria showed discrepancies between the two spotting test methods</li> </ul> <p> </p> <ul> <li>“Plaque_assays_R12_R14”: pictures of plaque assays where we tested the sensitivity of <em>E. coli</em> C R12 and R14 toward a subset of evolved phages</li> </ul> <p> </p> <ul> <li>“Plaque_assays_of_PhiX174R22T1c1/R28T1c1_vs_WT”: pictures of plaque assays where we tested the sensitivity of <em>E. coli</em> C WT toward phages infecting R22 and R28.</li> </ul> <p> </p> <ul> <li>“displayImage.R”. Script made to look for the desired combination of phage and bacterium.</li> </ul> <p> </p> <p>Finally, we include the raw OD measurments:</p> <p> </p> <ul> <li>“All_EcoliC_growth_raw_data.xlsx”: raw OD measurements of each <em>E. coli</em> C resistant strains used in this study.</li> </ul>
Data from: Lipidome modulation by dietary omega-3 polyunsaturated fatty acid supplementation or selective soluble epoxide hydrolase inhibition suppresses rough LPS-accelerated glomerulonephritis in lupus-prone mice
<p>Lipopolysaccharide (LPS)-accelerated autoimmune glomerulonephritis (GN) in lupus-prone NZBWF1 mice is a preclinical model that is potentially applicable for investigating lipidome-modulating interventions. LPS can be expressed as one of two chemotypes: smooth LPS (S-LPS) and rough LPS (R-LPS) which is devoid of O-antigen polysaccharide sidechain. Since these chemotypes differentially affect TLR4-mediated immune cell responses, these differences may influence GN induction. Therefore, we initially compared the effects of subchronic i.p. injection for 5 wk with 1) <em>Salmonella</em> S-LPS, 2) <em>Salmonella</em> R-LPS, or 3) saline vehicle (VEH) (Study 1) in female NZBWF1 mice. R-LPS induced robust elevations in blood urea nitrogen, proteinuria, and hematuria that were not evident in VEH- or S-LPS-treated mice. Histopathologic examination of R-LPS-treated mice one week after final injection further revealed more robust hypertrophy, hyperplasia, thickened membranes, lymphocytic accumulation containing B and T cells, and glomerular IgG deposition consistent with GN but not in VEH- or S-LPS-treated groups. R-LPS but not S-LPS induced spleen enlargement with lymphoid hyperplasia as well as modest inflammatory cell recruitment in the liver. We next employed our optimized R-LPS model to discern the impact of two lipidome-modulating interventions, omega-3 polyunsaturated fatty acid (PUFA) supplementation and soluble epoxide hydrolase (sEH) inhibition, on GN (Study 2). Specifically, the effects of consuming the omega-3 PUFA docosahexaenoic acid (DHA) (10 g/kg diet) and/or the sEH inhibitor TPPU (22.5 mg/kg diet) on R-LPS triggering were compared. Resultant blood fatty acid profiles and epoxy fatty acid concentrations reflected the anticipated DHA- and TPPU-mediated lipidome changes. The relative rank order of R-LPS-induced GN severity among groups fed experimental diets based on proteinuria, hematuria, histopathologic scoring, and glomerular IgG deposition was: VEH/CON < R-LPS/DHA ≈ R-LPS/TPPU <<< R-LPS/ TPPU+DHA ≈ R-LPS/CON. These interventions had modest to negligible effects on R-LPS-induced splenomegaly, plasma antibody responses, liver inflammation, and inflammation-associated kidney gene expression. Collectively, our results show for the first time that absence of O-antigenic polysaccharide in R-LPS is critical to accelerated GN in lupus-prone mice. Furthermore, intervention by lipidome modulation through DHA feeding or sEH inhibition suppressed R-LPS-induced GN; however, these ameliorative effects were greatly diminished upon combining the treatments.</p>
Immunohistochemistry of wild type and hjv-/- iron manipulated mouse livers and spleens treated with LPS or Hepcidin
<p><span>The iron hormone hepcidin is transcriptionally activated by iron or inflammation via distinct, partially overlapping pathways. We addressed how iron affects inflammatory hepcidin levels and the ensuing hypoferremic response. Dietary iron overload did not mitigate hepcidin induction in LPS-treated wt mice but prevented effective inflammatory hypoferremia. Likewise, LPS modestly decreased serum iron in hepcidin-deficient Hjv-/- mice, model of hemochromatosis. Synthetic hepcidin triggered hypoferremia in control but not iron-loaded wt animals. Furthermore, it dramatically decreased hepatic and splenic ferroportin in Hjv-/- mice on standard or iron-deficient diet, but only triggered hypoferremia in the latter. Mechanistically, iron antagonized hepcidin responsiveness by inactivating IRPs in the liver and spleen, to stimulate ferroportin mRNA translation. Prolonged LPS treatment eliminating ferroportin mRNA permitted hepcidin-mediated hypoferremia in iron-loaded mice. Thus, de novo ferroportin synthesis is critical determinant of serum iron and finetunes hepcidin-dependent functional outcomes. Our data uncover a crosstalk between hepcidin and IRE/IRP systems that controls tissue ferroportin expression and determines serum iron levels. Moreover, they suggest that hepcidin supplementation therapy is more efficient combined with iron depletion.</span></p>
Effect of Gamma Tocopherol Enriched Supplementation on Response to Inhaled LPS
ClinicalTrials.gov study NCT02104505. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of GSK2798745 on Alveolar Barrier Disruption in a Segmental Lipopolysaccharide (LPS) Challenge Model
ClinicalTrials.gov study NCT03511105. IPD Sharing: YES. Countries: 1. Publications: 1.
Immunohistochemistry of wild type and hjv-/- iron manipulated mouse livers and spleens treated with LPS or Hepcidin
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Data from: Lipidome modulation by dietary omega-3 polyunsaturated fatty acid supplementation or selective soluble epoxide hydrolase inhibition suppresses rough LPS-accelerated glomerulonephritis in lupus-prone mice
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Transcriptomic and metabolomic analysis identifies potential targets of HADHA lactylation in LPS-treated H9c2 cells
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Bulk RNAseq of chronic LPS treated female mouse pituitary across doses
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Untargeted metabolomics molecular features data for plasma of 20 Peromyscus leucopus and 20 Mus musculus treated with LPS or controls
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In vivo differentially-expressed genes in Peromyscus leucopus, Mus musculus, and Rattus norvegicus blood in response to LPS
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Pathway enrichments from untargeted metabolomics of plasma of Peromyscus leucopus and Mus musculus with or without LPS treatment
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