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Module-based functional pathway enrichment analysis of a protein-protein interaction network to study the effects of intestinal microbiota depletion in mice.

ABSTRACT: Complex communities of microorganisms play important roles in human health, and alterations in the intestinal microbiota may induce intestinal inflammation and numerous diseases. The purpose of this study was to identify the key genes and processes affected by depletion of the intestinal microbiota in a murine model. The Affymetrix microarray dataset GSE22648 was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified using the limma package in R. A protein-protein interaction (PPI) network was constructed for the DEGs using the Cytoscape software, and the network was divided into several modules using the MCODE plugin. Furthermore, the modules were functionally annotated using the PiNGO plugin, and DEG-related pathways were retrieved and analyzed using the GenMAPP software. A total of 53 DEGs were identified, of which 26 were upregulated and 27 were downregulated. The PPI network of these DEGs comprised 3 modules. The most significant module-related DEGs were the cytochrome P450 (CYP) 4B1 isozyme gene (CYP4B1) in module 1, CYP4F14 in module 2 and the tachykinin precursor 1 gene (TAC1) in module 3. The majority of enriched pathways of module 1 and 2 were oxidation reduction pathways (metabolism of xenobiotics by CYPs) and lipid metabolism-related pathways, including linoleic acid and arachidonic acid metabolism. The neuropeptide signaling pathway was the most significantly enriched functional pathway of module 3. In conclusion, our findings strongly suggest that intestinal microbiota depletion affects cellular metabolism and oxidation reduction pathways. In addition, this is the first time, to the best of our knowledge, that the neuropeptide signaling pathway is reported to be affected by intestinal microbiota depletion in mice. The present study provides a list of candidate genes and processes related to the interaction of microbiota with the intestinal tract.

SUBMITTER: Jia ZY

PROVIDER: S-EPMC4055453 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Module-based functional pathway enrichment analysis of a protein-protein interaction network to study the effects of intestinal microbiota depletion in mice.

Jia Zhen-Yi ZY Xia Yang Y Tong Danian D Yao Jing J Chen Hong-Qi HQ Yang Jun J

Molecular medicine reports 20140409 6

Complex communities of microorganisms play important roles in human health, and alterations in the intestinal microbiota may induce intestinal inflammation and numerous diseases. The purpose of this study was to identify the key genes and processes affected by depletion of the intestinal microbiota in a murine model. The Affymetrix microarray dataset GSE22648 was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified using the limma packag ...[more]

PMID: 24718810

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Project description:BACKGROUND:Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. RESULTS:Our approach naturally generalizes Fisher's exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting subnetwork of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. CONCLUSIONS:We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data andresults are available at http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/.

Project description:BackgroundQuantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.ResultsHerein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein-protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein-protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON's results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes.ConclusionPIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Project description:In spite of current medical treatment approaches, mortality of chronic heart failure (HF) remains high and novel treatment modalities are thus urgently needed. A recent theory proposes a possible impact of the intestinal microbiome on the incidence and clinical course of heart failure. This study sought to systematically investigate, if there are specific changes of the intestinal microbiome in heart failure patients.The intestinal microbiome of 20 patients with heart failure with reduced ejection fraction due to ischemic or dilated cardiomyopathy was investigated by applying high-throughput sequencing of the bacterial 16S rRNA gene. Microbial profiles were compared to those of matched controls in which heart failure was ruled out by clinical assessment and NT-proBNP serum levels (n = 20). According to the Shannon diversity index (which measures the intra-individual alpha-diversity) based on the distribution of operational taxonomic units (OTUs), HF cases showed a nominally significantly lower diversity index compared to controls (Pnom. = 0.01), and testing for genera abundance showed a tendency towards a decreased alpha diversity of HF patients. Beta-diversity measures (inter-individual diversity) revealed a highly significant separation of HF cases and controls, (e.g. Pweighted UniFracv = 0.004). Assessing the individual abundance of core measurable microbiota (CMM), a significant decrease of Coriobacteriaceae, Erysipelotrichaceae and Ruminococcaceae was observed on the family level. In line with that, Blautia, Collinsella, uncl. Erysipelotrichaceae and uncl. Ruminococcaceae showed a significant decrease in HF cases compared to controls on the genus level.Heart failure patients showed a significantly decreased diversity of the intestinal microbiome as well as a downregulation of key intestinal bacterial groups. Our data point to an altered intestinal microbiome as a potential player in the pathogenesis and progression of heart failure.

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Module-based functional pathway enrichment analysis of a protein-protein interaction network to study the effects of intestinal microbiota depletion in mice.

Publications

Module-based functional pathway enrichment analysis of a protein-protein interaction network to study the effects of intestinal microbiota depletion in mice.

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