Project description:The gastrointestinal microbiota is involved in the development of various diseases, such as obesity and diabetes, by affecting nutrient acquisition, energy balance, and metabolic signaling of the host. However, the underlying mechanisms of these host-microbiota interactions remain unclear. The aim of this study was to characterize effects of the microbiota on the host epithelium using a novel gastrointestinal model based on mouse organoids. We have explored the transcriptional response of organoids upon exposure to short chain fatty acids (SCFA) and products generated by two conspicuous members of the gastrointestinal microbiota; Akkermansia muciniphila and Faecalibacterium prausnitzii. We observed that A. muciniphila metabolites affect a large variety of transcription factors and genes involved in cellular lipid metabolism and growth, supporting previous in vivo findings. F. prausnitzii products exerted only a weak effect on the host transcription profile. While, A. muciniphila, and its main metabolite propionate, both stimulate fasting-induced adipose factor/angiopoietin-like protein 4 (Fiaf/Angptl4) and Ppar? expression, important regulators of lipolysis and satiety. This work illustrates that specific members of the microbiota and their metabolites differentially modulate epithelial transcription in mouse organoid lines. Organoid culture: In short, murine (WT C57BL/6) small intestinal crypts were isolated and embedded in 250 µl Matrigel (BD Biosciences) and submerged in 500 ul basal culture medium supplemented with penicillin/streptomycin, HEPES, Glutamax, N2, B27, N-acetylcysteine, and the murine growth factors EGF, noggin, and R-spondin-1 (ENR), as previously described (Sato et al., 2009. Nature 459:262–5). Organoids were passaged every 7 days with a 1:4 splitting ratio. Exposure to bacterial cultures and SCFA: A. muciniphila (ATTC BAA-835) was grown anaerobically at 37C in a basal liquid medium which contained (per liter of deionized water) the following: 0.4g KH2PO4, 0.669g, Na2HPO4 + 2H2O, 0.3g NH4CL, 0.3gNaCl, 0.1g MgCl2 + 6H2O, 10g casitone, 1mM L-threonine, 1 ml trace mineral solution, 5mM L-fucose, and 5mM D-glucose. F. prausnitzii strain A2-165 (DSM 17677) was grown anaerobically at 37ºC a liquid medium which contained (per liter of deionized water) the following: 10 g casitone, 2.5 g yeast extract, 4 g NaHCO3, 1 ml resazurine (0.1%w/v), 0.45g K2HPO4, 0.45g KH2PO4, 0.9g (NH4)2SO4, 0.9g NaCL, 0.09g MgSO4, 0.09g CaCL2, 2.5 mM acetate, 9 mM propionate, 1 mM n-valerate, 1 mM isovalerate, 1 mM isobutyrate, 0.28g KOH, 2.5 ml ethanol (95%), 10 mg haemin,, 10 ug biotin, 10 ug cobalamin, 30 ug para-aminobenzoic acid, 50 ug folic acid, 150 ug pyridoxamine, 1 g L-cysteine, and 25 mM glucose (Duncan et al., 2002. Int. J. Syst. Evol. Microbiol. 52:2141–6). The concentration of the following organic acids and sugars were determined in the culture medium before and after growth using a high performance liquid chromatography (HPLC) equipped with a Shodex Sugar SH-G and Shodex SH1821 column as has been described previously (Stams et al., 1993. Appl. Environ. Microbiol. 59:1114–9): glucose, mannose, galactose, L-fucose, glucose-N-acetylglucosamine, lactate, formate acetate, propionate, 1,2-propendiol and butyrate. Overnight-grown cultures were pelleted by centrifugation at 20 000 ×g for 10 min and supernatant was collected. At t=7 days after splitting organoids were exposed to 250 μl A. muciniphila supernatant, 250 μl unconditioned A. muciniphila culture medium, 85 μl F. prausnitzii supernatant, or 85 μl F. prausnitzii culture medium for 3 hours at 37ºC, prior to RNA extraction. 250 μl A. muciniphila supernatant and culture medium Short chain fatty acids (SCFA) sodium butyrate, sodium propionate, and sodium acetate (Sigma-Aldrich, Zwijndrecht, the Netherlands) were administered at a final concentration of 5 mM. At t=7 days after splitting mature organoids were exposed to individual SCFAs for 3 hours at 37ºC, prior to RNA extraction. Data is presented from n = 4 independent biological replicates from one line of organoids. total RNA was isolated from mouse intestinal organoids exposed for 3h to SCFA and strain-specific culture medium: Am- (organoids exposed to A. muciniphila culturing medium), Am+ (A. muciniphila-conditioned medium), Fp- (F. prausnitzii-culturing medium), Fp+ (F. prausnitzii-conditioned medium), Ace (acetate), But (butyrate), Pro (propionate) and Con (standard organoid culturing medium).

Project description:Qi2013 - IL-6 and IFN crosstalk model This model [BIOMD0000000544] describes the crosstalk between IFN-gamma and IL-6 induced signalling; it aims to outline mechanisms and factors that may control the interaction between both signalling pathways, discussing a role of heterodimer formation in signalling dysfunction. To account for the possibility of different IFNR and gp130 binding sites for STAT1 and STAT3, model 1 [BIOMD0000000543] assumes that there is no competition between STAT1 and STAT3 for the receptor complexes (includes two extra reactions). The reverse of this is true in model 2 [BIOMD0000000544] where it generally is assumed that there is competition between STAT1 and STAT3 for the receptor complexes. This model is described in the article: Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling. Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX. BMC Bioinformatics 2013; 14: 41 Abstract: BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. This model is hosted on BioModels Database and identified by: BIOMD0000000544. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Qi2013 - IL-6 and IFN crosstalk model (non-competitive) This model [BIOMD0000000543] describes the crosstalk between IFN-gamma and IL-6 induced signalling; it aims to outline mechanisms and factors that may control the interaction between both signalling pathways, discussing a role of heterodimer formation in signalling dysfunction. To account for the possibility of different IFNR and gp130 binding sites for STAT1 and STAT3, model 1 [BIOMD0000000543] assumes that there is no competition between STAT1 and STAT3 for the receptor complexes (includes two extra reactions). The reverse of this is true in model 2 [BIOMD0000000544] where it generally is assumed that there is competition between STAT1 and STAT3 for the receptor complexes. This model is described in the article: Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling. Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX. BMC Bioinformatics 2013; 14: 41 Abstract: BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. This model is hosted on BioModels Database and identified by: BIOMD0000000543. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

			Action	DRS
	SRR12321500.fastq.gz	Fastqsanger.gz
	SRR12321600.fastq.gz	Fastqsanger.gz
	SRR12321501.fastq.gz	Fastqsanger.gz
	SRR12321601.fastq.gz	Fastqsanger.gz
	SRR12321502.fastq.gz	Fastqsanger.gz

Dataset Information

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets