Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base—driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis. PLoS Comput Biol 8 2 : e
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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. A total of 53 DEGs were identified, of which 26 were upregulated and 27 were downregulated. 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. The intestinal microbiota is dominated by five bacterial phyla Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria and Verrucomicrobia and one Archaea Euryarchaeota 2.
These complex communities of microorganisms play an important role in metabolic, nutritional, physiological and immunological processes in the human body 3. Molecular characterization of the intestinal microbiota by phylogenetic approaches has received considerable attention in recent years and revealed a remarkable compositional stability and resilience in adult life, even after pervasive treatments with antibiotics 4. Species of the genera Bifidobacterium and Lactobacillus are particularly present in the colon of healthy individuals, and they are generally regarded as desirable, owing to the reduction of the neutral pH to a more acidic pH that they cause 5.
Changes in microbial community composition are closely associated with various diseases, such as allergic disease 6 , colorectal cancer 7 and intestinal inflammatory disease 8. Our understanding of intestinal microbiota and their importance for the human physiology has increased, owing to international research initiatives such as the MetaHIT project 1 and the Human Microbiome Project 9.
However, the development of simple protocols for the manipulation of intestinal microbiota in experimental animal models is still needed. Recently, a study focusing on the effects of intestinal microbiota depletion on the gut mucosa and epithelial gene expression was performed; depletion of the intestinal microbiota was achieved in mice by administering broad-spectrum antibiotics in drinking water The study reported that antibiotic treatment significantly reduced the expression of antimicrobial factors to a level similar to that of germ-free mice, and altered the expression of a total of genes in the colonic epithelium.
The expression of genes involved in the cell cycle was significantly altered, concomitant with reduced epithelial proliferative activity in situ , as assessed by Ki expression, which suggested that commensal microbiota drives cellular proliferation in the colonic epithelium Metabolites produced by the gut microbiota community from processes such as oxidation reduction and lipid metabolism have been reported to considerably affect intestinal functions 1.
The present study used a previously released microarray dataset 10 to assess the effects of intestinal microbiota depletion in mice, by focusing on the gene expression profiles of colonic intestinal epithelial cells in the presence and absence of intestinal microbiota.
These profiles were analyzed using a series of bioinformatic methods, including protein-protein interaction PPI network construction, module functional annotation and pathway enrichment analyses.
Further research on the mechanisms identified here as affected by the intestinal microbiota depletion is planned for a future study. Data from a total of 11 chips were analyzed, corresponding to colonic intestinal epithelial cell gene expression profiles of 5 replicates from mice with depleted intestinal microbiota and 6 replicates from control mice that were not treated with antibiotics germ-free.
The raw data were preprocessed using the Affy package in R Differential expression analysis between the 5 intestinal microbiota-depleted and the 6 control samples was performed using limma, a linear regression model software package available in R 12 , and multiple testing correction was performed using a Bayesian method To visualize the expression profiles of DEGs and all genes, unsupervised hierarchical clustering analysis was performed All associations available in STRING are provided with a probabilistic confidence score, which was derived by separately benchmarking groups of associations against the a manually curated functional classification scheme Each score represents a rough estimate of how likely a given association describes a functional linkage between two gene products.
GenMAPP is a powerful tool for graphically viewing microarray data in the context of pathway analysis in an intuitive manner for biologists, and it was previously used in the analysis of microarray data related to allergic disease The normalized expression values following preprocessing of the raw data are shown in Fig. Among these DEGs, 26 were upregulated and 27 were downregulated upon microbiota depletion.
Boxplot of normalized expression values for the dataset accession no. The data for the six control samples are presented on the left, and for the five samples with microbiota depletion on the right. The dotted line in the middle of each box represents the median of each sample, and its distribution among samples indicates the level of normalization of the data, with a nearly straight line revealing a fair normalization level.
Hierarchical clustering analysis was performed on the expression values of all genes and of the 53 DEGs. Clearly distinct expression patterns were observed between the microbiota-depleted and the control mice in both the total gene and DEG clustering analysis Fig.
Clustering analysis of gene expression values of A all genes and B of differentially expressed genes. The change of color from green to red represents the change in log FC from low to high. FC, fold change. The PPI network was constructed Fig.
The DEGs in module 1 Fig. The terms unsaturated fatty acid, lipid, cellular lipid and fatty acid metabolic process were the most significantly enriched functions in module 2, and the upregulated gene CYP4F14 Fig. Primary protein-protein interaction PPI network and selected modules.
Red- and green-color nodes represent products of up- and downregulated DEGs, respectively. Purple nodes denote products of genes predicted to interact with the DEGs. Functional annotation of the genes in the three modules using Gene Ontology GO terms. Characteristics of the most significant differentially expressed genes in the 3 modules. Module 1 was found to be significantly enriched for a total of 12 pathways, module 2 for 5 and module 3 for 2 Table III.
Pathway enrichment analysis of differentially expressed genes in the three modules based on information from the Kyoto Encyclopedia of Genes and Genomes KEGG pathways database for Mus musculus mmu.
The collective genome of the human intestinal microbiota was estimated to contain 3. Intestinal microbiota mostly use fermentation to generate energy, converting sugars, in part, to short-chain fatty acids, which are used by the host as an energy source 1.
To understand the impact of intestinal microbiota on human health, it is crucial to assess their potential function. The present study identified a total of 53 DEGs, comprising 26 upregulated and 27 downregulated genes upon depletion of the intestinal microbiota in mice. Important differences in gene expression were observed between intestinal microbiota-depleted and control mice in hierarchical clustering analysis. The majority of enriched pathways of module 1 and 2 were oxidation reduction metabolism of xenobiotics by CYPs and lipid e.
In addition, the neuropeptide signaling pathway was the most significantly enriched pathway in module 3. Two types of functions of intestinal microbiota have been identified in a previous study, those required in all bacteria and those potentially specific to the gut 1. Functions of the first category relate to central metabolic pathways for example, carbon metabolism and amino acid synthesis and to important protein complexes RNA and DNA polymerase, ATP synthase, general secretory apparatus 1.
The putative gut-specific functions include those involved in adhesion to host proteins collagen, fibrinogen, fibronectin , or in harvesting sugars of the globo-series glycolipids, which are carried on blood and epithelial cells 1.
In the present study, most of module 1-related DEGs were involved in oxidation reduction and metabolic processes such as metabolism of xenobiotics by CYPs, and the majority of module 2-related DEGs were involved in lipid metabolic processes, such as lipid metabolic process and arachidonic acid metabolism. These results suggest that the intestinal microbiota is involved in numerous metabolic and biosynthetic processes, but has particularly important roles in the regulation of lipid biosynthesis and in oxidation-reduction processes, as also indicated by previous studies 22 — In rats and rabbits, the CYP4B1 protein was shown to play an important role in mutagenic activation of procarcinogens in the organs Most of organic xenobiotics require metabolic activation to electrophilic intermediates to produce adverse carcinogenic effects.
Specific enzymes of the CYP superfamily are involved in the formation of reactive metabolites from certain substrates that are predicted or known occupational and environmental carcinogens A new prodrug-activating enzyme system for pharmacogenic therapy of experimental brain tumors based on the rabbit CYP4B1 protein was previously described CYP4Fs are a subfamily of enzymes involved in arachidonic acid metabolism and showing the highest catalytic activity towards leukotriene LT B4, a potent chemoattractant involved in inflammation.
The CYP4B1 and CYP4F14 genes were identified as significantly upregulated in the present study, which, in combination with previous reports, suggests that intestinal microbiota depletion may lead to inflammation and cancer in the body. It is notable that modules 1 and 2 were both enriched for the processes of arachidonic and linoleic acid metabolism. Arachidonic acid is a pivotal signaling molecule, involved in the initiation and propagation of diverse signaling cascades regulating inflammation, pain and homeostatic functions It is metabolized by three enzymatic pathways: the cyclooxygenase pathway produces prostanoid, the lipoxygenase pathway yields monohydroxy compounds and LTs, while the CYP epoxygenase pathway generates hydroxy and epoxyeicosanoids.
There is increasing evidence that some of these metabolic products play critical roles in cardiovascular disease Linoleic acid is predominant in dairy products and plant oils such as flax seed, and animal studies have reported a reduction in intra-abdominal fat and an enhanced gain in fat-free mass upon linoleic acid supplementation; another study reported linoleic acid-mediated whole-body fat loss in overweight men and women; there have also been some concerns that linoleic acid can promote oxidative stress and induce hepatic lipid accumulation 30 — Based on these studies and the present findings on arachidonic and linoleic acid metabolism, the two processes appear to play a key role in human health and to be closely linked to the balance of intestinal microbiota.
In contrast to the reported effects of intestinal microbiota on oxidation reduction and lipid metabolism 30 — 32 , an association between intestinal microbiota and the neuropeptide signaling pathway has not been previously reported.
In our study, it is notable that the neuropeptide signaling pathway was the most significantly enriched pathway in module 3. This gene encodes a neurotransmitter of the central and peripheral nervous system 33 , and the protein has additionally been associated with immunologic and inflammatory processes The gut and the brain are closely connected organs, and their interaction plays an important role not only in gastrointestinal function, but also in certain feeling states and in intuitive decision making 35 ; alterations in this interaction have been associated with a wide range of disorders, including functional, inflammatory gastrointestinal, and eating disorders.
It has been reported that healthy humans and rats produce autoantibodies directed against appetite-regulating peptide hormones and neuropeptides, suggesting that these autoantibodies may play physiological roles in hunger- and satiety-related pathways Gut-related antigens including those produced by the intestinal microflora, may affect the production of these autoantibodies, which might represent a new link between the gut and the regulation of appetite.
We thus argue that the depletion of the intestinal microflora in mice may lead to impaired neuropeptide signaling. In conclusion, our findings strongly suggest that intestinal microbiota depletion affects metabolism, oxidation reduction and neuropeptide signaling pathways in mice, involving a number of genes and interactions.
Numerous diseases, as well as aging, can be induced by depletion of the intestinal microflora, and therefore, the dynamic equilibrium of the intestinal microflora plays a key role in human health. The neuropeptide signaling pathway was first reported in the present study to be affected by the depletion of the intestinal microflora, a result which reveals a potential link between the intestinal bacteria and the nervous system.
However, further experimentation and additional studies are needed to confirm this link; such studies are expected to enhance our understanding of the interactions between the bacteria of the intestinal microflora and their host environment. National Center for Biotechnology Information , U. Mol Med Rep. Published online Apr 9. Author information Article notes Copyright and License information Disclaimer. China, E-mail: moc. Received Oct 16; Accepted Feb The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited.
This article has been cited by other articles in PMC. Abstract Complex communities of microorganisms play important roles in human health, and alterations in the intestinal microbiota may induce intestinal inflammation and numerous diseases.
Keywords: intestinal microbiota depletion, protein-protein interaction network, modules, pathway enrichment analysis. Identification and clustering analysis of differentially expressed genes DEGs The raw data were preprocessed using the Affy package in R Results Identification of DEGs The normalized expression values following preprocessing of the raw data are shown in Fig. Open in a separate window. Figure 1. Figure 2.
After running AltAnalyze to identify differentially or alternatively expressed genes, it is recommended that the users explore these results along biological pathways. Within AltAnalyze users can select the GO-Elite option to obtain over-representation results for a number of ontologies, pathway and gene-set databases. Since this interface has limited options for pathway visualization, the user may want to use more sophisticated pathway visualization programs. Two examples are listed below. Compatibility and installation information can be found here. In addition, it can create new pathways and has a number of sophisticated options for streamlined data visualization.
An Introduction to Pathway Analysis with GenMAPP