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Quantification of Transcriptome Responses of the Rumen Epithelium to Butyrate Infusion using RNA-seq Technology.

Baldwin RL, Wu S, Li W, Li C, Bequette BJ, Li RW - Gene Regul Syst Bio (2012)

Bottom Line: An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10(-11)).Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate.Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health.

View Article: PubMed Central - PubMed

Affiliation: USDA-ARS, Bovine Functional Genomics Laboratory, Beltsville, MD, USA.

ABSTRACT
Short-chain fatty acids (SCFAs), such as butyrate, produced by gut microorganisms, play a critical role in energy metabolism and physiology of ruminants as well as in human health. In this study, the temporal effect of elevated butyrate concentrations on the transcriptome of the rumen epithelium was quantified via serial biopsy sampling using RNA-seq technology. The mean number of genes transcribed in the rumen epithelial transcriptome was 17,323.63 ± 277.20 (±SD; N = 24) while the core transcriptome consisted of 15,025 genes. Collectively, 80 genes were identified as being significantly impacted by butyrate infusion across all time points sampled. Maximal transcriptional effect of butyrate on the rumen epithelium was observed at the 72-h infusion when the abundance of 58 genes was altered. The initial reaction of the rumen epithelium to elevated exogenous butyrate may represent a stress response as Gene Ontology (GO) terms identified were predominantly related to responses to bacteria and biotic stimuli. An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10(-11)). Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate. Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health.

No MeSH data available.


A regulatory gene network controlled by FOS.Notes: The network was inferred using ARACNE at a combined stringent cutoff of an error tolerance ɛ = 0.10 and a P-value threshold of mutual information (MI) at 5.0 × 10−11. FOS had 4 direct interactions (the first neighbors) and 32 indirect interactions. The expression of all genes in this network at the mRNA level was significantly regulated by butyrate. The green/yellow color with gene symbols represents transcription factors.
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f4-grsb-6-2012-067: A regulatory gene network controlled by FOS.Notes: The network was inferred using ARACNE at a combined stringent cutoff of an error tolerance ɛ = 0.10 and a P-value threshold of mutual information (MI) at 5.0 × 10−11. FOS had 4 direct interactions (the first neighbors) and 32 indirect interactions. The expression of all genes in this network at the mRNA level was significantly regulated by butyrate. The green/yellow color with gene symbols represents transcription factors.

Mentions: The biological interpretation of high-throughput expression data generated using microarrays or RNA-seq technology requires both differential expression and differential network analyses.38 Many transcriptional regulators exert their impact on biological functions via post-transcriptional mechanisms with subtle or no apparent changes at mRNA expression level detectable by tools for assessing differential expression alone. However, differential network analysis relies on powerful computational tools to extract accurate regulatory gene networks reflecting causal interactions underlying biological processes or phenotypes. Of several algorithms available, those based on information theory, including estimating mutual information values, such as ARACNE26,39 perform well in inferring global gene networks, especially for smaller sample sizes.40,41 ARACNE is based on the assumptions that the expression level of a given gene is a random variable and the mutual relationships between them can be derived by statistical dependences.42 Our results provided further support for the utility of this approach in constructing regulatory gene networks that depict phenotypes and regulation of biological processes. An example from this current study is the regulatory network controlled by FBJ murine osteosarcoma viral oncogene homolog, or c-fos (FOS), which was significantly down-regulated by butyrate in both in vitro and in vivo models. As a transcription factor, FOS dimerizes with another oncogene JUN to form the AP-1 complex, which regulates transcription of a diverse range of genes and is implicated in many biological processes including cell proliferation and differentiation as well as tumor transformation and progression. In the current data set, ARACNE inferred a network of four direct interactions (1st neighbors) for FOS and 32 indirect interactions (Fig. 4). All four direct interactions, cingulin (CGN), heparin-binding epidermal growth factor-like growth factor (HBEGF), intermediate filament family orphan 2 (IFFO2), and jun proto-oncogene (JUN), were up-regulated by butyrate (at both P < 10−6 and FDR < 10−5). Moreover, of 24 genes in the 2nd neighbors category, all were also regulated by butyrate, including three transcription factors, JUN, upstream transcription factor 2, c-fos interacting (USF2), and REST corepressor 1 (RCOR1). GO analysis identified GO terms significantly enriched in this network, including SMAD binding (GO:0046332 and GO:0070412), SMAD protein signal transduction (GO:0060395), and transforming growth factor (TGF)-β receptor signaling (GO:0007179). FOS binding at the TGF-β1 promoter proximal AP-1 site is required for TGF-β1 production by colon carcinoma cells.43 Indeed, previous studies have shown that SMAD proteins cooperate with FOS/JUN complex to mediate TGF-β-induced transcription.44 Thus, ARACNE correctly inferred a direct interaction between FOS and JUN (Fig. 4) as well as interaction between JOS and HBEGF. HBEGF plays a pivotal role in mediating the early cellular response to intestinal injury by serving as a potent cytoprotective factor.45 Other experimental evidence also provides a strong support of a direct interaction between FOS and HBEGF.45,46


Quantification of Transcriptome Responses of the Rumen Epithelium to Butyrate Infusion using RNA-seq Technology.

Baldwin RL, Wu S, Li W, Li C, Bequette BJ, Li RW - Gene Regul Syst Bio (2012)

A regulatory gene network controlled by FOS.Notes: The network was inferred using ARACNE at a combined stringent cutoff of an error tolerance ɛ = 0.10 and a P-value threshold of mutual information (MI) at 5.0 × 10−11. FOS had 4 direct interactions (the first neighbors) and 32 indirect interactions. The expression of all genes in this network at the mRNA level was significantly regulated by butyrate. The green/yellow color with gene symbols represents transcription factors.
© Copyright Policy - open-access
Related In: Results  -  Collection

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f4-grsb-6-2012-067: A regulatory gene network controlled by FOS.Notes: The network was inferred using ARACNE at a combined stringent cutoff of an error tolerance ɛ = 0.10 and a P-value threshold of mutual information (MI) at 5.0 × 10−11. FOS had 4 direct interactions (the first neighbors) and 32 indirect interactions. The expression of all genes in this network at the mRNA level was significantly regulated by butyrate. The green/yellow color with gene symbols represents transcription factors.
Mentions: The biological interpretation of high-throughput expression data generated using microarrays or RNA-seq technology requires both differential expression and differential network analyses.38 Many transcriptional regulators exert their impact on biological functions via post-transcriptional mechanisms with subtle or no apparent changes at mRNA expression level detectable by tools for assessing differential expression alone. However, differential network analysis relies on powerful computational tools to extract accurate regulatory gene networks reflecting causal interactions underlying biological processes or phenotypes. Of several algorithms available, those based on information theory, including estimating mutual information values, such as ARACNE26,39 perform well in inferring global gene networks, especially for smaller sample sizes.40,41 ARACNE is based on the assumptions that the expression level of a given gene is a random variable and the mutual relationships between them can be derived by statistical dependences.42 Our results provided further support for the utility of this approach in constructing regulatory gene networks that depict phenotypes and regulation of biological processes. An example from this current study is the regulatory network controlled by FBJ murine osteosarcoma viral oncogene homolog, or c-fos (FOS), which was significantly down-regulated by butyrate in both in vitro and in vivo models. As a transcription factor, FOS dimerizes with another oncogene JUN to form the AP-1 complex, which regulates transcription of a diverse range of genes and is implicated in many biological processes including cell proliferation and differentiation as well as tumor transformation and progression. In the current data set, ARACNE inferred a network of four direct interactions (1st neighbors) for FOS and 32 indirect interactions (Fig. 4). All four direct interactions, cingulin (CGN), heparin-binding epidermal growth factor-like growth factor (HBEGF), intermediate filament family orphan 2 (IFFO2), and jun proto-oncogene (JUN), were up-regulated by butyrate (at both P < 10−6 and FDR < 10−5). Moreover, of 24 genes in the 2nd neighbors category, all were also regulated by butyrate, including three transcription factors, JUN, upstream transcription factor 2, c-fos interacting (USF2), and REST corepressor 1 (RCOR1). GO analysis identified GO terms significantly enriched in this network, including SMAD binding (GO:0046332 and GO:0070412), SMAD protein signal transduction (GO:0060395), and transforming growth factor (TGF)-β receptor signaling (GO:0007179). FOS binding at the TGF-β1 promoter proximal AP-1 site is required for TGF-β1 production by colon carcinoma cells.43 Indeed, previous studies have shown that SMAD proteins cooperate with FOS/JUN complex to mediate TGF-β-induced transcription.44 Thus, ARACNE correctly inferred a direct interaction between FOS and JUN (Fig. 4) as well as interaction between JOS and HBEGF. HBEGF plays a pivotal role in mediating the early cellular response to intestinal injury by serving as a potent cytoprotective factor.45 Other experimental evidence also provides a strong support of a direct interaction between FOS and HBEGF.45,46

Bottom Line: An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10(-11)).Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate.Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health.

View Article: PubMed Central - PubMed

Affiliation: USDA-ARS, Bovine Functional Genomics Laboratory, Beltsville, MD, USA.

ABSTRACT
Short-chain fatty acids (SCFAs), such as butyrate, produced by gut microorganisms, play a critical role in energy metabolism and physiology of ruminants as well as in human health. In this study, the temporal effect of elevated butyrate concentrations on the transcriptome of the rumen epithelium was quantified via serial biopsy sampling using RNA-seq technology. The mean number of genes transcribed in the rumen epithelial transcriptome was 17,323.63 ± 277.20 (±SD; N = 24) while the core transcriptome consisted of 15,025 genes. Collectively, 80 genes were identified as being significantly impacted by butyrate infusion across all time points sampled. Maximal transcriptional effect of butyrate on the rumen epithelium was observed at the 72-h infusion when the abundance of 58 genes was altered. The initial reaction of the rumen epithelium to elevated exogenous butyrate may represent a stress response as Gene Ontology (GO) terms identified were predominantly related to responses to bacteria and biotic stimuli. An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10(-11)). Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate. Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health.

No MeSH data available.