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564 results for “small intestine”
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
RNA-Seq data from: Hox genes modulate physical forces to differentially shape small and large intestinal epithelia
<p>Hox genes are highly conserved, master regulators of spatial patterning in the embryo, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of the small and large intestines early in embryogenesis, which ultimately leads to their specialization in both form and function. While the midgut forms villi, the hindgut develops flat, brain-like sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements and mathematical modeling, we demonstrate that the posterior Hox gene Hoxd13 regulates biophysical phenomena that shape the hindgut lumen. We further show that Hoxd13 acts through the TGFβ pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme; together, these features lead to hindgut surface buckling. TGFβ, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional genetic identity that direct posterior intestinal morphogenesis. </p> <p>To identify genes and pathways that are directly or indirectly regulated by Hoxd13 to affect posterior gut morphogenesis in the chick, we compared mesodermal transcriptomes of wild-type midgut and hindgut intestinal samples, as well as mesodermal samples from a Hoxd13-overexpressing midgut at E12 and E14. Tissues were dissected and endoderm layers were removed manually before RNA extraction and downstream processing. Unbiased clustering was used to identify genes commonly differentially expressed in the hindgut and Hoxd13-misexpressing midgut. This submission contains bulk RNA-seq raw data (fastq.bz2 files) and processed .txt files with read counts. Experiment information is provided in .xlsx Metadata file used for NCBI GEO submission.</p>
Full summary statistics of mixQTL for GTEx v8 Small_Intestine_Terminal_Ileum
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Figure 2 in First report of the genus Brachylaima Dujardin, 1843 (Trematoda: Brachylaimidae) from the small intestine of common myna (Acridotheres tristis) of district Swabi, Khyber Pakhtunkhwa, Pakistan
Figure 2. Photomicrographs of B. fuscatum (Rudolphi, 1819): a) Entire specimen. b) Fore body enlarged. c) Eggs.
Figure 4 in First report of the genus Brachylaima Dujardin, 1843 (Trematoda: Brachylaimidae) from the small intestine of common myna (Acridotheres tristis) of district Swabi, Khyber Pakhtunkhwa, Pakistan
Figure 4. Photomicrographs of B. sabahense Fischthal and Kuntz, 1974: a) Entire specimen. b) Fore body enlarged. c) Eggs.
Data from: Sustained intestinal epithelial monolayer wound closure after transient application of a FAK-activating small molecule
<p>M64HCl, which has drug-like properties, is a water-soluble Focal Adhesion Kinase (FAK) activator that promotes murine mucosal healing after ischemic or NSAID-induced injury. Since M64HCl has a short plasma half-life in vivo (less than two hours), it has been administered as a continuous infusion with osmotic minipumps in previous animal studies. However, the effects of more transient exposure to M64HCl on monolayer wound closure remained unclear. Herein, we compared the effects of shorter M64HCl treatment in vitro to continuous treatment for 24 hours on monolayer wound closure. We then investigated how long FAK activation and downstream ERK1/2 activation persist after two hours of M64HCl treatment in Caco-2 cells. M64HCl concentrations immediately after washing measured by mass spectrometry confirmed that M64HCl had been completely removed from the medium while intracellular concentrations had been reduced by 95%.<strong> </strong>Three-hour and four-hour<strong> </strong>M64HCl (100 nM) treatment promoted epithelial sheet migration over 24 hours similar to continuous 24-hour exposure. 100nM M64HCl did not increase cell number. Exposing cells twice with 2-hr exposures of M64HCl during a 24-hour period had a similar effect. Both FAK inhibitor PF-573228 (10 µM) and ERK kinase (MEK) inhibitor PD98059 (20 µM) reduced basal wound closure in the absence of M64HCl, and each completely prevented any stimulation of wound closure by M64HCl. Rho kinase inhibitor Y-27632 (20 µM) stimulated Caco-2 monolayer wound closure but no further increase was seen with M64HCl in the presence of Y-27632. M64HCl (100 nM) treatment for 3 hours stimulated Rho kinase activity. M64HCl decreased F-actin in Caco-2 cells. Furthermore, a two-hour treatment with M64HCl (100 nM) stimulated sustained FAK activation and ERK1/2 activation for up to 16 and hours 24 hours, respectively. These results suggest that transient M64HCl treatment promotes prolonged intestinal epithelial monolayer wound closure by stimulating sustained activation of the FAK/ERK1/2 pathway. Such molecules may be useful to promote gastrointestinal mucosal repair even with a relatively short half-life.</p>
Figure 3 in First report of the genus Brachylaima Dujardin, 1843 (Trematoda: Brachylaimidae) from the small intestine of common myna (Acridotheres tristis) of district Swabi, Khyber Pakhtunkhwa, Pakistan
Figure 3. Diagrams of B. sabahense Fischthal & Kuntz, 1974: a) Entire specimen. b) Eggs.
Figure 1 in First report of the genus Brachylaima Dujardin, 1843 (Trematoda: Brachylaimidae) from the small intestine of common myna (Acridotheres tristis) of district Swabi, Khyber Pakhtunkhwa, Pakistan
Figure 1. Diagrams of B. fuscatum (Rudolphi, 1819): a) Entire specimen. b) Eggs.
Small intestinal microbiota data of pigs receiving control or pharmaceutical levels of ZincOxide
<p>To improve our understanding of host and intestinal microbiome interaction, this research investigated the effects of a high level zinc oxide in the diet as model intervention on the intestinal microbiome and small intestinal functionality in clinically healthy post-weaning piglets.</p> <p>In study 1 piglets received either a high concentration of zinc as zinc oxide (Zn, 2690 mg/kg) or a low Zn concentration (100 mg/kg) in the diet during the post weaning period (d 14-23). We investigated the effects on the piglet’s small intestinal microbiome and functionality of intestinal tissue. In study 2 we investigated the impact of timing of the dietary zinc intervention, i.e. between d 0-14 and/or d 14-23 post weaning, and the consecutive effects on the piglet’s intestinal functionality. We acquired data on the microbiota composition in small intestinal digesta and gene expression profiles of both jejunum and ileum tissue.</p> <p>Overall, we observed differences in the small intestinal functionality during the post weaning period between piglets receiving a diet with a low or high concentration ZnO content.</p>
Pilot Feasibility Study of Fecal Microbiota Transplant for the Treatment of Small Intestinal Bacterial Overgrowth
ClinicalTrials.gov study NCT05967871. IPD Sharing: YES. Countries: 1. Publications: 25.
Is Chronic Abdominal Pain in Pediatric Patients Due to Small Intestinal Bacterial Overgrowth?
ClinicalTrials.gov study NCT00619970. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Sustained intestinal epithelial monolayer wound closure after transient application of a FAK-activating small molecule
Open the record for dataset details and reuse information.
Data from: High-molecular-weight polymers from dietary fiber drive aggregation of particulates in the murine small intestine
The lumen of the small intestine (SI) is filled with particulates: microbes, therapeutic particles, and food granules. The structure of this particulate suspension could impact uptake of drugs and nutrients and the function of microorganisms; however, little is understood about how this suspension is re-structured as it transits the gut. Here, we demonstrate that particles spontaneously aggregate in SI luminal fluid ex vivo. We find that mucins and immunoglobulins are not required for aggregation. Instead, aggregation can be controlled using polymers from dietary fiber in a manner that is qualitatively consistent with polymer-induced depletion interactions, which do not require specific chemical interactions. Furthermore, we find that aggregation is tunable; by feeding mice dietary fibers of different molecular weights, we can control aggregation in SI luminal fluid. This work suggests that the molecular weight and concentration of dietary polymers play an underappreciated role in shaping the physicochemical environment of the gut.
Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis
<p>Codes and single cell data for "<strong>Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis</strong>" paper.</p> <p><strong>File: Tunel_Lyve1_Data.xlsx </strong>– quantifications of Tunel+Lyve1+ cells for n=4 NECs and n=4 neonatal samples.</p> <p><strong>Folder: Cell_ranger_output</strong></p> <p>Data produced using 10x sequencing.</p> <p>This folder contains all the Cell Ranger output raw files from all 11 subjects used for single cell RNA sequencing (5x Neonatal- S1056, S1127, S1212, S1214, S1082, 6x NECs- S1021, S1074, S1095, S1109, S1155, S1193).</p> <p>The neonatal subjects had two 10X runs (except subject 1082) - one done on CD45 negatively selected small intestinal cells and the other on non-selected small intestinal cells.</p> <p>The NEC subjects had one run each (subject 1074 was sequenced twice) with no selection due to low yield in the selected runs.</p> <p><strong>Folder: Seurat_pipeline</strong></p> <p>Done with Seurat package version 3.2.2</p> <p>This folder contains R scripts for Seurat analysis and script for background (BG) subtraction of the data.</p> <ol> <li>BG_subtraction_before_seurat.R - Script that creates a BG subtracted matrix for each CellRanger output before Seurat analysis.</li> <li>seurat_pipline_all_cells_NEC.R - Script for Seurat analysis for the full atlas.</li> <li>Final_seurat_obj_after_filtration_all_cells.rds – rds file of final Seurat object of all cells after pipeline filtration</li> <li>Files description in “RDS_files_for_seurat_pipeline” folder: <ol> <li>list_UMIs_after_BG_subtruction.rds – rds file containing a list of all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> <li>list_metadata_after_BG_subtruction.rds – rds file containing a list of metadata for all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> </ol> </li> </ol> <p><strong>Folder: Deconvolution_analysis</strong></p> <p>Done on Matlab R2019b</p> <p>This folder contains the script for deconvolution analysis and its input files.</p> <ol> <li>Deconvolution_parsing_NEC_for_zenodo.m - Matlab script to create box plots of deconvolution results and their q- values.</li> <li>Files description: <ol> <li>deconvolution_original8_clusters.mat – structure of deconvolution results. Signature cells were split into the 8 major cell type groups shown in figure 1.</li> <li>cellanneal_split_clust_results.mat – structure of deconvolution results. Signature cells were split into sub groups for each cell type group.</li> </ol> </li> </ol> <p><strong>Folder: Ligand-receptor_interaction_analysis</strong></p> <p>This folder contains matlab and R scripts for ligand-receptor interaction analysis.</p> <ol> <li>run_permutations_for_lig_rec_ratio_tensor_Zenodo.m – main script to run ligand-receptor analysis.</li> <li>calculate_ratio_ligand_receptor.m – function used by the main script.</li> <li>Interactions_visualizations_heatmaps.R – R script for heatmap visualization.</li> <li>Files description in “Mat_files_for_analysis” folder: <ol> <li>all_cells_split_for_interactions.mat – single cell structure of all cells. Cells are split into subgroups for ligand receptor analysis.</li> <li>ramilowsky.mat – ligand-receptor lists taken from Ramilowski et al. 2015 to use in main script and function.</li> <li>tensor_real_q100permutations.mat – output mat file of the interactions ratio, q values and ligand-receptor names for heatmaps visualization in R (input file for “Interactions_visualizations_heatmaps.R”).</li> </ol> </li> </ol>
sample1_scTCRseq of T cells from the small intestine of germ-free mice_raw sequence data
<p>sample1_scTCRseq of T cells from the small intestine of germ-free mice_raw sequence data</p>
sample2_scRNAseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data
<p>sample2_scRNAseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data</p>
sample2_scTCRseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data
<p>sample2_scTCRseq of T cells from the small intestine of hCom1 colonized mice_raw sequence data</p>
sample3_scRNaseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data
<p>sample3_scRNaseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data</p>
sample3_scTCRseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data
<p>sample3_scTCRseq of T cells from the small intestine of hCom2 colonized mice_raw sequence data</p>
sample1_scRNAseq of T cells from the small intestine of germ-free mice_raw sequence data
<p>sample1_scRNAseq of T cells from the small intestine of germ-free mice_raw sequence data</p>
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International Brain Laboratory public data
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OpenNeuro
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