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132 results for “gut analysis”
Mummichog (Fundulus heteroclitus) counts and gut content analysis from lift trap transect collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The lift traps were used to capture mummichogs accessing the high marsh platform. Mummichogs were collected to study the effect of marsh-edge geomorphology on mummichog distribution and foraging. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Mummichog (Fundulus heteroclitus) gut content analysis from Breder trap transect collections in tidal creeks associated with long term fertilization experiments, Rowley, MA.
At PIE, mummichog (Fundulus heteroclitus) use the spring-cycle high tides to access the flooded high marsh platform and eat invertebrate prey, coupling the high marsh and aquatic creek food webs by gathering energy produced on the high marsh and making it available to the aquatic food web. Changes in the geomorphology of saltmarsh creek edges greatly influence the survival, biomass, and resource use of mummichog populations. Here we use gut content analysis assess the diet of mummichog on the high marsh platform during a flooding spring-cycle tide in July 2018 across 3 PIE creeks known to present different geomorphologic patterns in their low marsh zones. These data allow us to quantify the amount of terrestrial invertebrate prey mummichog consume on a single flooding tide and determine the impact altered low marsh geomorphology has on the trophic relationships in PIE food webs. These mummichog were captured in Breder traps; information about the consumer communities captured in these traps was recorded separately (LTE-TIDE-BrederTrap-Demographics). These data were included in part of the study “Habitat decoupling via saltmarsh creek geomorphology alters connection between spatially-coupled food webs” (Lesser et al. 2020) and were a portion of an MBL REU project.
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Gut Analysis Toolbox: Data and code associated with JCS manuscript
<p>The data and python code in jupyter notebooks are associated with the manuscript: <strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <ul> <li><strong>FigS1_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. S1D,E.</li> <li><strong>Fig3_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. 3D-N. <ul> <li>The images and analysis files associated with analysis in GAT are also uploaded: CalR_CalB_GAT_analysis.zip</li> <li>The images used in this analysis are from EXP174 in this dataset: <a href="https://zenodo.org/records/7236748">https://zenodo.org/records/7236748</a></li> </ul> </li> </ul>
Assembly-based analysis of the infant gut microbiome reveals novel ubiquitous plasmids
<p>Assembly-based plasmids found in the gut microbiome of 12 infants born in Norway (BabyBiome project).</p>
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis
<p>Bioinformatic code, data files, raw figures and data accessibility table associated to the manuscript "Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis".</p>
TableS1 of ML-based predictive gut microbiome analysis for health assessment
<p>Complete list of species associated with the COVID and Control cohort (ANOVA F score > 1). Comparison with respect to the original set of species used by Gupta <em>et al.</em>, and ANOVA-F values are reported.</p>
Comparative analysis of gut microbiome of mangrove brachyuran crabs revealed patterns of phylosymbiosis and codiversification
Open the record for dataset details and reuse information.
Data from: Spider webs, stable isotopes and molecular gut content analysis: multiple lines of evidence support trophic niche differentiation in a community of Hawaiian spiders
1. Adaptive radiations are typically characterized by niche partitioning among their constituent species. Trophic niche partitioning is particularly important in predatory animals, which rely on limited food resources for survival. 2. We test for trophic niche partitioning in an adaptive radiation of Hawaiian Tetragnatha spiders, which have diversified in situ on the Hawaiian Islands. We focus on a community of nine species belonging to two different clades, one web building and the other actively hunting, which co-occur in wet forest on East Maui. We hypothesize that trophic niches differ significantly both 1) among species within a clade, indicating food resource partitioning, and 2) between the two clades, corresponding with their differences in foraging strategy. 3. To assess niches of the spider species, we measure a) web architecture, the structure of the hunting tool, and b) site choice, the physical placement of the web in the habitat. We then test whether differences in these parameters translate into meaningful differences in trophic niche by measuring c) stable isotope signatures of carbon and nitrogen in the spiders' tissues, and d) gut content of spiders based on metabarcoding data. 4. We find significant interspecific differences in web architecture and site choice. Importantly, these differences are reflected in stable isotope signatures among the five web-building species, as well as significant isotopic differences between web-builders and active hunters. Gut content data also show interspecific and inter-clade differences. Pairwise overlaps of web architecture between species are positively correlated with overlaps of isotopic signature. 5. Our results reveal trophic niche partitioning among species within each clade, as well as between the web-building and actively hunting clades. Based on the correlation between web architecture and stable isotopes, it appears that the isotopic signatures of spiders' tissues are influenced by architectural differences among their webs. Our findings indicate an important link between web structure, microhabitat preference and diet in the Hawaiian Tetragnatha.
Arthropod food webs in the foreland of a retreating glacier: Gut content analysis and structural equation modeling (SEM)
<p>Below- and above-ground arthropod communities were explored at a glacier foreland area in low Arctic Southwest Greenland aiming for a better understanding of the mechanisms behind the arthropod succession driven by increasing temperatures in the context of an Arctic climate change scenario. Arthropods were sampled in 2015 and 2016 along a downslope transect where the microclimate became warmer downhill a chronosequence towards a climax vegetation. The arthropod data sets were analyzed in relation to an environmental data set. Bottom-up controlled population developments were important in the early phase of the vegetation development while top-down prevailed in the later phase of the vegetation development. The shift from bottom-up to top-down cascades between arthropod predators and their potential prey populations was mainly driven by increasing temperatures away from the glacier. Structural equation modeling (SEM) shows bottom-up and top-down controlled food chains as bottom-up control was important for spider and harvestman populations while top-down control was important for ground beetle populations. These mechanisms are closely related to the hunting strategies of the predators as bottom-up mechanisms are connected to a sit-and-wait behavior while top-down mechanisms are related to active-search behavior. The SEM analyzes were supported by DNA metabarcoding as well as by the literature. A consequence of the strong top-down cascades in the later phase of the succession is high rates of intra-guild predation (IGP) among all arthropod predators. Particularly in the guts of the linyphiid spider, <em>Collinsia holmgreni </em>Thorell 1871, trophic linkages to other linyphiid and lycosid spiders were detected. The IGP ratio of <em>C. holmgreni</em> was negatively correlated with the activity density of available ground-living prey. Probably as a consequence of the high IGP among the linyphiid spiders, cold-adapted linyphiid species like <em>C. holmgreni</em> decreased in numbers downhill and became extinct in the warmer climax vegetation, where lycosid spiders dominated. SEM shows that the declining activity densities of the soil fauna, such as collembolans and mites, due to predation, are responsible for the increase in organic matter content in the topsoil.</p>
Data from: Dietary partitioning among three cryptobentic reef fish mesopredators revealed by visual analysis, metabarcoding of gut content, and stable isotope analysis
<p>Understanding how mesopredators partition their diet and the identity of consumed prey can assist in understanding the ecological role predators and prey play in ecosystem trophodynamics. Here, we assessed the diet of three common coral reef mesopredators; <em>Pseudochromis flavivertex</em>, <em>Pseudochromis fridmani</em>, and <em>Pseudochromis olivaceus</em> from the family Pseudochromidae, commonly known as dottybacks, using a combination of i) visual stomach content analysis, ii) stomach content DNA metabarcoding (18S, COI), and iii) stable isotope analysis (δ<sup>15</sup>N, δ<sup>13</sup>C). In addition, <em>P. flavivertex</em> is found in two distinct color morphs in the Red Sea, providing an opportunity to analyze intra-morph differences. These techniques revealed partitioning in the dietary composition and resource use among species. Arthropods comprised the main dietary component of <em>P. flavivertex</em> (18S > 60%; COI > 10%), and <em>P. olivaceus</em> (18S = 57.2%) while <em>P. fridmani</em> ingested predominantly mollusks (18S = 51.3%, COI = 24.6%). Despite being small predators, microplastics were found in the gut content of some of these fishes. Stable isotope analysis showed differences in species' isotopic niche breadth and trophic position. <em>Pseudochromis olivaceus</em> presented the largest isotopic niche (SEA<sub>C</sub> = 1.61‰<sup>2</sup>), while <em>P. fridmani</em> showed the smallest isotopic niche (SEA<sub>C</sub> = 0.45‰<sup>2</sup>) among species. Although the two techniques used for stomach content analysis did not show differences in the diet within color morphs of <em>P. flavivertex</em>, they differed in the isotopic niche and resource use. Despite our limited sampling, our findings provide evidence of species-specific differences in the trophic ecology of dottybacks and demonstrate their important role as predators of cryptic invertebrates and small fishes. This study highlights the importance of combining several approaches (short-term: visual analysis and DNA metabarcoding; and long-term: isotope analysis) when assessing the feeding habits of coral reef fish, as they provide complementary information necessary to delimit their niches and understand the role that small mesopredators play in coral reef ecosystems.</p>
Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs
<p><strong>Background:</strong></p> <p>Sesarmid crabs dominate mangrove habitat as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory is not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove forest ecosystems. Although endogenous cellulases expressions were reported in crab species, it remains unknown if the endogenous enzymes alone can complete the whole lignocellulolytic pathway, or they also depend on the contribution from their intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.</p> <p><strong>Results:</strong></p> <p>Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab species gut microbiome have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation in mangrove crabs and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. The herbivorous and detritivorous crabs showed highly similar CAZyme composition compared to dissimilarities observed in taxonomic profiles observed in both groups, suggesting a stronger selection force to gut microbiota by its functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of the gut microbiota in supplementing nitrogen that is deficient in plant diet.</p> <p><strong>Conclusions:</strong></p> <p>Endosymbiotic cellulolytic microbes play an important role in lignocellulose degradation in most crab species but their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity for digestion of mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in mangrove crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrate species.</p>
Image datasets associated with Gut Analysis Toolbox
<div>The images are sample image datasets associated with the software: <a href="https://gut-analysis-toolbox.gitbook.io/docs/">Gut Analysis Toolbox (GAT)</a>.</div> <div>The dataset contains immunofluorescence images of enteric neurons and glia labeled with different markers. The data is mostly from mouse and human colon or small intestine. </div> <div>Channels corresponding to Hu labelling can be used for segmenting enteric neurons in GAT. </div> <div>Channels corresponding to GFAP (enteric glia) or neurons with markers labelling the cell body and processes (ChAT, Calbindin, Calretinin) can be used as a ganglia marker for segmenting the ganglia</div> <div>The data is two-dimensional (2D) with some images having multiple channels. The data is mostly in tif format, except for one dataset that is czi (Fiji using bioformats or aicspylibczi in Python). Calcium imaging data is 2D+Time.</div> <div> </div> <div>Data curated by: <a href="https://www.linkedin.com/in/rajapradeep/">Pradeep Rajasekhar, Walter and Eliza Hall Institute of Medical Research</a>, Australia</div> <h2><strong>Data from<a href="https://www.monash.edu/mips/themes/drug-discovery-biology/labs/inm"> INM lab, Monash University</a> (mouse images)</strong></h2> <p><strong>Immunofluorescence images</strong></p> <ul> <li> 181107_ms_distal_colon_GFAP_Hu_40X.tif <ul> <li>Channel 1: GFAP</li> <li>Channel 2: Hu</li> </ul> </li> </ul> <div> <ul> <li>181107_ms_distal_colon_nNOS_GFAP_Hu_40X.tif (Same as above, but got an extra channel)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: nNOS</li> <li>Channel 2: GFAP</li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div>In both images above, GFAP can be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li>ms_distal_colon_Hu_20X.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>ms_distal_colon_Hu_40X_1.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>Tilescan_GAT_ms_distal_colon_MP_hu.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <h3><strong>Calcium imaging data (video)</strong></h3> <div>• calcium_imaging_mouse_distal_colon_25X.tif</div> <div>Tissue was incubated with calcium dye Fluo8-AM calcium dye (Myenteric wholemount from distal colon of mouse)</div> <div>142 frames in total acquired at 1.162 frames per second. At frame 52, 100 uM of ATP is added which causes enteric glia, neurons and blood vessels to respond. This causes slow drifting in the field of view.</div> <div> </div> <div>• mouse_GCamp_calcium_movement.tif</div> <div> </div> <div>Wnt1-GCaMP3 mouse where GCamP3 is a genetically encoded calcium sensor expressed by enteric neurons and enteric glia</div> <div>Imaging performed at Monash University. 745 frames in total acquired at 1.162 frames per second with drifting over time</div> <div>Tissue source: <a href="https://biomedicalsciences.unimelb.edu.au/sbs-research-groups/anatomy-and-physiology-research/neuroscience/development-of-the-enteric-nervous-system">Stamp & Hao laboratory, University of Melbourne.</a></div> <div> </div> <h2><strong>Images from McQuade Lab, University of Melbourne</strong></h2> <ul> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse colon)</li> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse ileum) <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div> <ul> <li>ms_distal_colon_nNOS_Hu_10X.czi (This is a .czi file which can be opened in Fiji using bioformats or aicspylibczi in Python)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <h2><strong>Images from public repository (SPARC)</strong>:</h2> <ul> <li>DYM_22_7_Pr_Chat_BYFP_DIN_GFP-g_nNOS-m_VIP-r_Hu-b.tif is a crop from File 100 05-07-2019 DYM 22 7 Pr Chat%3BYFP DIN GFP-g nNOS-m VIP-r Hu-b (Mouse Proximal Colon)</li> <li>DYM_22_7_Pr_Hu_crop.tif is a crop from above. <ul> <li>Channel 1: Choline acetyltransferase</li> <li>Channel 2: nNOS</li> <li>Channel 3: Calretinin</li> <li>Channel 4: Hu (pan-neuronal marker)</li> </ul> </li> </ul> <div> Channel 1 and 3 can be used as ganglia segmentation channels in GAT using DeepImageJ.</div> <div> </div> <div> <ul> <li>146_02_14_20DYM8_6_mouse_Mid_Chat-g CalB-r CalR-b_max.tif is from File 146 02-14-20 DYM 8 6 Mid Chat-g CalB-r CalR-b (Mouse mid colon)</li> </ul> </div> <div> No Hu staining, any channel could be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li> <ul> <li>Channel 1: ChAT</li> <li>Channel 2: Calbindin</li> <li>Channel 3: Calretinin</li> </ul> </li> </ul> </div> <h3><strong>Reference</strong>:</h3> <div>Thanks goes to Marthe Howard for depositing the data in the SPARC repository.</div> <div>Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium.<a href="https://doi.org/10.26275/9FFG-482D"> https://doi.org/10.26275/9FFG-482D</a></div> <div>**************</div> <h2><strong>Multiplex data (Flinders University)</strong></h2> <div><strong>Multiplexing_H2202Desc_Layer 1_Ganglia1_Hu.zip </strong>is from:</div> <div><a href="https://pubmed.ncbi.nlm.nih.gov/37355216/">Chen, B. N., Humenick, A., Yew, W. P., Peterson, R. A., Wiklendt, L., Dinning, P. G., Spencer, N. J., Wattchow, D. A., Costa, M., & Brookes, S. J. H. (2023). Types of Neurons in the Human Colonic Myenteric Plexus Identified by Multilayer Immunohistochemical Coding. Cellular and molecular gastroenterology and hepatology, 16(4), 573–605.</a></div> <div>This data is a myenteric wholemount from the descending colon of a Human. It has 14 different markers, 6 different rounds of staining. Every round has pan-neuronal marker Hu as a reference marker.There are 19 images. The filenames follow the convention:</div> <div> </div> <div>H2202Desc_<em>layer num</em>_<em>ganglia num</em>_<em>markername</em>.tif </div> <div><em>H2202 </em>is the sample name, <em>Desc </em>means descending colon</div> <div>Here <em>layer num</em> corresponds to the round of staining, so Layer3, means its the 3rd round of staining.</div> <div><em>ganglia num</em> is specified as multiple ganglia can be imaged from same tissue. </div> <div><em>markername</em> corresponds to the marker used. </div> <div> </div> <div>Markers used are: Hu, 5HT, ChAT, NOS, CGRP, Enk, SP, Somat, VACht, NPY, Calbindin, Calretinin, NF, VIP</div> <div> </div> <div><strong>Abbreviations:</strong></div> <div> </div> <ul> <li>Hu: Pan-neuronal marker</li> <li>5HT: Serotonin (5-Hydroxytryptamine)</li> <li>ChAT: Choline acetyltransferase</li> <li>nNOS: neuronal Nitric Oxide Synthase (NOS in these images are actually nNOS)</li> <li>CGRP: Calcitonin Gene-Related Peptide</li> <li>Enk: Enkephalin</li> <li>SP: Substance P</li> <li>Somat: Somatostatin</li> <li>VACht: Vasoactive Intestinal Peptide (VIP) </li> <li>NPY: Neuropeptide Y</li> <li>NF: neurofilament 200 </li> </ul>
Comparative analysis of Parkinson's and inflammatory bowel disease gut microbiomes reveals shared butyrate-producing bacteria depletion
<p><strong>Abstract: </strong>Epidemiological studies reveal that inflammatory bowel disease (IBD) is associated with an increased risk of Parkinson’s disease (PD). Gut dysbiosis has been documented in both PD and IBD, however it is currently unknown whether gut dysbiosis underlies the epidemiological association between both diseases. To identify shared and distinct features of the PD and IBD microbiome, we recruited 54 PD, 26 IBD, and 16 healthy control individuals and performed the first joint analysis of gut metagenomes. Larger, publicly available PD and IBD metagenomic datasets were also analyzed to validate and extend our findings. Depletions in short-chain fatty acid (SCFA)-producing bacteria, including <em>Roseburia intestinalis, Faecalibacterium prausnitzii, Anaerostipes hadrus</em>, and <em>Eubacterium rectale</em>, as well depletion in SCFA-synthesis pathways were detected across PD and IBD datasets, suggesting that depletion of these microbes in IBD may influence the risk for PD development.</p> <p><strong>Zenodo contents:</strong> In this Zenodo archive we provide the post-QC and taxonomic and functional profiling "Source Data" used in all downstream analyses to generate tables and figures seen in our manuscript. We also provide the link to our GitHub repository where we have stored the code used to perform the bioinformatic processing of the shotgun metagenomic sequences and stastical analyses. Individual sample raw shotgun metagenomic sequences and metadata from our UFPF dataset are available on NCBI Sequence Read Archive (SRA) under BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/1096686">PRJNA1096686</a>. </p>
Gut microbiome analysis of high fat diet- and control-fed on PXR-KO mice
<p>Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease due to the current epidemics of obesity and diabetes. The pregnane X receptor (PXR) is a xenobiotic-sensing nuclear receptor known for trans-activating liver genes involved in drug metabolism and transport, and more recently implicated in energy metabolism. The gut microbiota can modulate the host xenobiotic biotransformation and contribute to the development of obesity. While the male sex confers a higher risk for NAFLD than women before menopause, the mechanism remains unknown. We hypothesized that the presence of PXR promotes obesity by modifying the gut-liver axis in a sex-specific manner. Male and female C57BL/6 (wild-type/WT) and PXR-knockout (PXR-KO) mice were fed control or high fat diet (HFD) for 16-weeks. Serum parameters, liver histopathology, transcriptomic profiling, 16S-rDNA sequencing, and bile acid (BA) metabolomics were performed. PXR enhanced HFD-induced weight gain, hepatic steatosis and inflammation especially in males, accompanied by PXR-dependent up-regulation in hepatic genes involved in microbial response, inflammation, oxidative stress, and cancer; PXR-dependent increase in intestinal Firmicutes/Bacteroides ratio (hallmark of obesity) and the pro-inflammatory Lactobacillus, as well as a decrease in the anti-obese Allobaculum and the anti-inflammatory Bifidobacterum, with a PXR-dependent reduction of beneficial BAs in liver. The resistance to NAFLD in females may be explained by PXR-dependent decrease in pro-inflammatory bacteria (<em>Ruminococcus gnavus and Peptococcaceae</em>). In conclusion, PXR exacerbates hepatic steatosis and inflammation accompanied by obesity- and inflammation-prone gut microbiome signature, suggesting that gut microbiome may contribute to PXR-mediated exacerbation of NAFLD.</p>
SHIP gut microbiome dataset for GP2 analysis
<p>The Study of Health in Pomerania (SHIP) is a population-based study. SHIP consists of the two independent cohorts: SHIP-START and SHIP-TREND. The aim of the study is the investigation of common risk factors, subclinical disorders and manifest diseases in the population of Northeast Germany. Determination of gut microbiota profiles using 16S rRNA gene sequencing was performed as described before in detail [1]. In brief, DNA was extracted from fecal samples stored in a DNA-stabilizing EDTA-buffer using the PSP Spin Stool DNA Kit (Stratec Biomedical AG, Birkenfeld, Germany) following the manufacturer's instructions and isolates were stored at -20 °C degrees until sequencing of the V1/V2 region of the bacterial 16S rRNA gene using the 27F and the 338R primers on a MiSeq platform (Illumina, San Diego, USA).</p> <p>[1] Frost, F., Kacprowski, T., Rühlemann, M., Bülow, R., Kühn, J.-P., Franke, A., Heinsen, F.-A., Pietzner, M., Nauck, M., Völker, U., Völzke, H., Aghdassi, A. A., Sendler, M., Mayerle, J., Weiss, F. U., Homuth, G., & Lerch, M. M. (2019). Impaired Exocrine Pancreatic Function Associates With Changes in Intestinal Microbiota Composition and Diversity. Gastroenterology, 156(4), 1010–1015. https://doi.org/10.1053/j.gastro.2018.10.047</p> <p> </p>
Supporting isotopic data for: Amino acid isotope analysis reveals variation in gut microbial contribution to host protein metabolism in a wild small mammal community
<p>Herbivory is a dominant feeding strategy among animals, yet herbivores are often protein limited. The gut microbiome is hypothesized to help maintain host protein balance by provisioning essential macromolecules, but this has never been tested in wild consumers. Using amino acid carbon (δ<sup>13</sup>C) and nitrogen (δ<sup>15</sup>N) isotope analysis, we estimated the proportional contributions of essential amino acids (AA<sub>ESS</sub>) synthesized by gut microbes to five co-occurring desert rodents representing herbivorous, omnivorous, and insectivorous functional groups. We found that herbivorous rodents occupying lower trophic positions (<em>Dipodomys </em>spp.) routed a substantial proportion (~40–50%) of their AA<sub>ESS</sub> from gut microbes, while higher trophic level omnivores (<em>Peromyscus </em>spp.) and insectivores (<em>Onychomys arenicola</em>) obtained most of their AA<sub>ESS</sub> (~58%) from plant-based energy channels but still received ~20% of their AA<sub>ESS</sub> from gut microbes. These findings empirically demonstrate that gut microbes play a key functional role in host protein metabolism in wild animals. </p>
Synbiotics Impact on Insulin and TNF-α in MAFLD: a Gut Microbiota Profile Analysis
ClinicalTrials.gov study NCT06585982. IPD Sharing: NO. Countries: 1. Publications: 15.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.