Ontology highlight
ABSTRACT:
PROVIDER: PRJNA995187 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25296888_1.fastq.gz | Fastqsanger.gz | |||
| SRR25296888_2.fastq.gz | Fastqsanger.gz | |||
| SRR25296889_1.fastq.gz | Fastqsanger.gz | |||
| SRR25296889_2.fastq.gz | Fastqsanger.gz | |||
| SRR25296890_1.fastq.gz | Fastqsanger.gz |
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Project description:It is well known that host-microbes and immunity interactions are influenced by dietary patterns, as well as daily environmental light-dark (LD) cycles that entrain circadian rhythms in the host. Emerging data has highlighted the importance of diet patterns and timing on the interaction among circadian rhythms, gut microbiome, and immunity, however, their impacts on LD cycles are less reported. Therefore, we aim to study how LD cycles regulate the homeostatic crosstalk between gut microbiome, hypothalamic and hepatic circadian clock oscillations and immunity. We hypothesized that different environmental LD cycles: (1) constant darkness, LD0/24; (2) short light, LD8/16; (3) normal LD cycle, LD12/12; (4) long light, LD16/8; and (5) constant light, LD24/0, may affect immunity and metabolism to varying degrees. Therefore, 240 mice were managed with chow diets (CD) and antibiotics treatments (ABX) under five different LD cycles for 42 days. The colonic (co) and cecum (ce) contents were obtained for studying their impacts on gut microbiome using 16S rRNA sequencing.
2023-03-10 | GSE226852 | GEO
Project description:Leber2015 - Mucosal immunity and gut
microbiome interaction during C. difficile infection
This model is described in the article:
Systems Modeling of
Interactions between Mucosal Immunity and the Gut Microbiome
during Clostridium difficile Infection.
Leber A, Viladomiu M, Hontecillas R,
Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera
J.
PLoS ONE 2015; 10(7): e0134849
Abstract:
Clostridium difficile infections are associated with the use
of broad-spectrum antibiotics and result in an exuberant
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immunity during C. difficile infection from initiation through
expansion to resolution, we built a computational model of the
mucosal immune response to the bacterium. The model was
calibrated using data from a mouse model of C. difficile
infection. The model demonstrates a crucial role of T helper 17
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luminal commensal bacteria populations in the clearance of C.
difficile and colonic pathology, whereas regulatory T (Treg)
cells responses are associated with the recovery phase. In
addition, the production of anti-microbial peptides by inflamed
epithelial cells and activated neutrophils in response to C.
difficile infection inhibit the re-growth of beneficial
commensal bacterial species. Computational simulations suggest
that the removal of neutrophil and epithelial cell derived
anti-microbial inhibitions, separately and together, on
commensal bacterial regrowth promote recovery and minimize
colonic inflammatory pathology. Simulation results predict a
decrease in colonic inflammatory markers, such as neutrophilic
influx and Th17 cells in the colonic lamina propria, and length
of infection with accelerated commensal bacteria re-growth
through altered anti-microbial inhibition. Computational
modeling provides novel insights on the therapeutic value of
repopulating the colonic microbiome and inducing regulatory
mucosal immune responses during C. difficile infection. Thus,
modeling mucosal immunity-gut microbiota interactions has the
potential to guide the development of targeted fecal
transplantation therapies in the context of precision medicine
interventions.
This model is hosted on
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and identified by:
BIOMD0000000583.
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2024-09-02 | BIOMD0000000583 | BioModels
Project description:Bovine colonic contents Raw sequence reads
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Project description:The aim of this study was to examine changes in the miRNA transcriptional profile of spermatozoa in Holstein-Friesian bulls. Spermatozoa from bulls divergent for feritlity status were used for this study with 10 High fertility bulls and 10 Low fertility bulls. RNA was extracted and subsequently subjected to miRNAseq analysis. 13 miRNA were identified as differentially expressed in the spermatozoa between bulls of high and low feritlity status.
2022-09-13 | GSE196750 | GEO