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accession-icon GSE59494
Comparison of the effects of five dietary fibers on mucosal transcriptional profiles, and luminal microbiota composition and SCFA concentrations in murine colon
  • organism-icon Mus musculus
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

Consumption of diets rich in fibers has been associated with several beneficial effects on gastrointestinal health. However, detailed studies on the molecular effects of fibers in colon are limited. In this study we investigated and compared the influence of five different fibers on the mucosal transcriptome, and luminal microbiota and SCFA concentrations in murine colon. Mice were fed diets enriched with fibers that differed in carbohydrate composition, namely inulin (IN), oligofructose (FOS), arabinoxylan (AX), guar gum (GG), resistant starch (RS) or a control diet (corn starch) for 10 days. Gene expression profiling revealed the regulation of specific, but also overlapping sets of epithelial genes by each fiber, which on a functional level were mainly linked to cell cycle and various metabolic pathways including fatty acid oxidation, tricarboxylic acid cycle, and electron transport chain. In addition, the transcription factor PPAR was predicted to be a prominent upstream regulator of these processes. Microbiota profiles were distinct per dietary fiber, but the fibers IN, FOS, AX and GG induced a common change in microbial groups. All dietary fibers, except resistant starch, increased SCFA concentrations but to a different extent. Multivariate data integration revealed strong correlations between the expression of genes involved in energy metabolism and the relative abundance of bacteria belonging to the group of Clostridium cluster XIVa, that are known butyrate producers. These findings illustrate the potential of multivariate data analysis to unravel simple relationships in complex systems.

Publication Title

Comparison of the effects of five dietary fibers on mucosal transcriptional profiles, and luminal microbiota composition and SCFA concentrations in murine colon.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE85913
Gender and strain dependent differences in intestinal immunology correlate with differences in microbiota composition
  • organism-icon Mus musculus
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Sex and strain dependent differences in mucosal immunology and microbiota composition in mice.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE85911
Gender and strain dependent differences in intestinal immunology correlate with differences in microbiota composition (colon)
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

A dysbiosis in the intestinal microbiome plays a role in the pathogenesis of several immunological diseases. These diseases often show a gender bias, suggesting gender differences in immune responses and in the intestinal microbiome. We hypothesized that gender differences in immune responses are associated with gender differences in microbiota. We demonstrated mouse strain dependent gender differences in the intestinal microbiome. Interestingly, a cluster of colonic genes (related to humoral and cell-mediated immune responses) correlated oppositely with microbiota species abundant in B6 females and in BALB/c males. This suggests that with different genetic backgrounds, gender associated immune responses are differentially regulated by microbiota. The net result was the same, since both mouse strains showed similar gender induced differences in immune cell populations in the mesenteric lymph nodes. Therefore, host-microbe interactions might be more complicated than assumed, as bacterial-species adaptations might be highly dependent on the genetic make-up of the individual.

Publication Title

Sex and strain dependent differences in mucosal immunology and microbiota composition in mice.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE85912
Gender and strain dependent differences in intestinal immunology correlate with differences in microbiota composition (ileum)
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

A dysbiosis in the intestinal microbiome plays a role in the pathogenesis of several immunological diseases. These diseases often show a gender bias, suggesting gender differences in immune responses and in the intestinal microbiome. We hypothesized that gender differences in immune responses are associated with gender differences in microbiota. We demonstrated mouse strain dependent gender differences in the intestinal microbiome. Interestingly, a cluster of colonic genes (related to humoral and cell-mediated immune responses) correlated oppositely with microbiota species abundant in B6 females and in BALB/c males. This suggests that with different genetic backgrounds, gender associated immune responses are differentially regulated by microbiota. The net result was the same, since both mouse strains showed similar gender induced differences in immune cell populations in the mesenteric lymph nodes. Therefore, host-microbe interactions might be more complicated than assumed, as bacterial-species adaptations might be highly dependent on the genetic make-up of the individual.

Publication Title

Sex and strain dependent differences in mucosal immunology and microbiota composition in mice.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE94515
Aging-induced decline in mucus thickness in mice is associated with changes in microbiota composition and immunity and is sex dependent
  • organism-icon Mus musculus
  • sample-icon 26 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

A mucus layer covers and protects the intestinal epithelial cells from direct contact with microbes. This mucus layer not only prevents inflammation but also plays an essential role in microbiota colonization, indicating the complex interplay between mucus composition-microbiota and intestinal health. However, it is unknown whether the mucus layer is influenced by age or sex and whether this contributes to reported differences in intestinal diseases in males and females or with ageing. Therefore, in this study we investigated the effect of age on mucus thickness, intestinal microbiota composition and immune composition in relation to sex. The ageing induced shrinkage of the colonic mucus layer was associated with bacterial penetration and direct contact of bacteria with the epithelium in both sexes. Additionally, several genes involved in the biosynthesis of mucus were downregulated in old mice, especially in males, and this was accompanied by a decrease in abundances of various Lactobacillus species and unclassified Clostridiales type IV and XIV and increase in abundance of the potential pathobiont Bacteroides vulgatus. The changes in mucus and microbiota in old mice were associated with enhanced activation of the immune system as illustrated by a higher percentage of effector T cells in old mice. Our data contribute to a better understanding of the interplay between mucus-microbiota-and immune responses and ultimately may lead to more tailored design of strategies to modulate mucus production in targeted groups.

Publication Title

The effect of age on the intestinal mucus thickness, microbiota composition and immunity in relation to sex in mice.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE104063
Aged gut microbiota contributes to systemical inflammaging after transfer to germ-free mice
  • organism-icon Mus musculus
  • sample-icon 46 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

Advanced age is associated with chronic low-grade inflammation, which is usually referred to as inflammaging. Elderly are also known to have an altered gut microbiota composition. However, whether inflammaging is a cause or consequence of an altered gut microbiota composition is not clear. In this study gut microbiota from young or old conventional mice was transferred to young germ-free mice. Four weeks after gut microbiota transfer immune cell populations in spleen, Peyers patches, and mesenteric lymph nodes from conventionalized germ-free mice were analyzed by flow cytometry. In addition, whole-genome gene expression in the ileum was analyzed by microarray. Gut microbiota composition of donor and recipient mice was analyzed with 16S rDNA sequencing. Here we show by transferring aged microbiota to young germ-free mice that certain bacterial species within the aged microbiota promote inflammaging. This effect was associated with lower levels of Akkermansia and higher levels of TM7 bacteria and Proteobacteria in the aged microbiota after transfer. The aged microbiota promoted inflammation in the small intestine in the germ-free mice and enhanced leakage of inflammatory bacterial components into the circulation was observed. Moreover, the aged microbiota promoted increased T cell activation in the systemic compartment. In conclusion, these data indicate that the gut microbiota from old mice contributes to inflammaging after transfer to young germ-free mice.

Publication Title

Aged Gut Microbiota Contributes to Systemical Inflammaging after Transfer to Germ-Free Mice.

Sample Metadata Fields

Sex, Specimen part, Treatment

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accession-icon GSE7141
mRNA expression analysis of undifferentiated Dicer +/- (D4) and Dicer -/- (27H10) embryonic cell lines
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We have analyzed the transcript expression levels in Dicer heterozygous and Dicer knock-out embryonic stem (ES) cells in order to identify which transcripts are regulated by RNAi pathway in mouse ES cells.

Publication Title

MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE8503
mRNA expression analysis of undifferentiated Dicer -/- (27H10) embryonic stem cells after miRNA transfection
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We have analyzed the transcript expression levels in Dicer knock-out embryonic stem (ES) cells 24 hours after transfection with either control siRNA agains Renilla luciferase or miRNA Mimics (Dharmacon) of mmu-miR-290 cluster members in order to identify primary targets of miR-290 cluster miRNAs.

Publication Title

MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP026540
Comparative Transcriptomics of Soybean Near Isogenic Lines in Response to Phytophthora Sojae
  • organism-icon Glycine max
  • sample-icon 22 Downloadable Samples
  • Technology Badge IconIllumina HiScanSQ

Description

Phytophthora root and stem rot (PRR) of soybean, caused by Phytophthora sojae, is effectively controlled by Rps genes in soybean. Rps genes are race-specific, yet the mechanism of resistance, as well as susceptibility, remains largely unclear. Taking advantage of RNA-seq technology, we sequenced the transcriptomes of 10 near isogenic lines (NIL), each with a unique Rps gene, and the recurrent susceptible parent 'Williams'. A total of 4330 differentially expressed genes (DEGs) were identified in 'Williams' while 2075 to 5499 DEGs were identified in each NIL. Comparisons between the NILs and 'Williams' allowed classification of two major groups of DEGs of interest: incompatible reaction associated genes (IRAGs) and compatible reaction associated genes (CRAGs). Hierarchical cluster analysis divided NILs into three clusters: Cluster I (Rps1-a), Cluster II (Rps1-b, 1-c and 1-k) and Cluster III (Rps3-a, 3-b, 3-c, 4, 5, and 6). Heatmap analysis, along with GO analysis suggested that the diversity of clusters for NILs were likely due to variation of the number of DEGs and the intensity of gene expression, rather than functional differentiation. Further analysis suggested that transcription factors might play pivotal role in determination of the cluster pattern, and that WRKY family were strongly associated with incompatible reactions. Analysis of IRAGs and CRAGs with putative functions suggested that the regulation of many phytohormone signaling pathways were associated with incompatible or compatible interactions with potential crosstalk between each other. As such, our study provides an in depth view of both incompatible and compatible interactions between soybean and P. sojae, which provides further insight into the mechanisms involved in host-pathogen interactions. Overall design: 22 samples were sequenced, 11 inoculated with P. sojae, the other 11 were mock-inoculated

Publication Title

Molecular response to the pathogen Phytophthora sojae among ten soybean near isogenic lines revealed by comparative transcriptomics.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE101102
Gene expression response to altered gravity, simulated gravity and hypergravity in human T cells
  • organism-icon Homo sapiens
  • sample-icon 87 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Stability of gene expression in human T cells in different gravity environments is clustered in chromosomal region 11p15.4.

Sample Metadata Fields

Cell line

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...

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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