Human gut metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA240658 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1185970.fastq.gz | Fastqsanger.gz | |||
| SRR1185980.fastq.gz | Fastqsanger.gz | |||
| SRR1185990.fastq.gz | Fastqsanger.gz | |||
| SRR1186000.fastq.gz | Fastqsanger.gz | |||
| SRR1186020.fastq.gz | Fastqsanger.gz |
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Project description:Significant gut microbiota heterogeneity exists amongst UC patients though the clinical implications of this variance are unknown. European and South Asian UC patients exhibit distinct disease risk alleles, many of which regulate immune function and relate to variation in gut microbiota β-diversity. We hypothesized ethnically distinct UC patients exhibit discrete gut microbiotas with unique luminal metabolic programming that influence adaptive immune responses and relate to clinical status. Using parallel bacterial 16S rRNA and fungal ITS2 sequencing of fecal samples (UC n=30; healthy n=13), we corroborated previous observations of UC-associated depletion of bacterial diversity and demonstrated significant gastrointestinal expansion of Saccharomycetales as a novel UC characteristic. We identified four distinct microbial community states (MCS 1-4), confirmed their existence using microbiota data from an independent UC cohort, and show they co-associate with patient ethnicity and degree of disease severity. Each MCS was predicted to be uniquely enriched for specific amino acid, carbohydrate, and lipid metabolism pathways and exhibited significant luminal enrichment of metabolic products from these pathways. Using a novel in vitro human DC/T-cell assay we show that DC exposure to patient fecal water led to MCS -specific changes in T-cell populations, particularly the Th1:Th2 ratio, and that patients with the most severe disease exhibited the greatest Th2 skewing. Thus, based on ethnicity, microbiome composition, and associated metabolic dysfunction, UC patients may be stratified in a clinically and immunologically meaningful manner, providing a platform for the development of FMC-focused therapy. Fecal microbiome was assessed with Affymetrix PhyloChip arrays from patients with ulcerative colitis and healthy controls.
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Project description:Leber2015 - Mucosal immunity and gut
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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
inflammatory response, leading to nosocomial diarrhea, colitis
and even death. To better understand the dynamics of mucosal
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
(Th17) effector responses in the colonic lamina propria and
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.
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2024-09-02 | BIOMD0000000583 | BioModels