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Co-Expression Analysis of Blood Cell Genome Expression to Preliminary Investigation of Regulatory Mechanisms in Uremia.

ABSTRACT: BACKGROUND Uremia involves a series of clinical manifestations and is a common syndrome that occurs in nearly all end-stage kidney diseases. However, the exact genetic and/or molecular mechanisms that underlie uremia remain poorly understood. MATERIAL AND METHODS In this case-control study, we analyzed whole-genome microarray of 75 uremia patients and 20 healthy controls to investigate changes in gene expression and cellular mechanisms relevant to uremia. Gene co-expression network analysis was performed to construct co-expression networks using differentially expressed genes (DEGs) in uremia. We then determined hub models of co-expressed gene networks by MCODE, and we used miRNA enrichment analysis to detect key miRNAs in each hub module. RESULTS We found nine co-expressed hub modules implicated in uremia. These modules were enriched in specific biological functions, including "proteolysis", "membrane-enclosed lumen", and "apoptosis". Finally, miRNA enrichment analysis to detect key miRNAs in each hub module found 15 miRNAs that were specifically targeted to uremia-related hub modules. Of these, miRNA-21-3p and miRNA-210-3p have been identified in other studies as being important for uremia. CONCLUSIONS In summary, our study connected biological functions, genes, and miRNAs that underpin the network modules that can be used to elucidate the molecular mechanisms involved in uremia.

SUBMITTER: Cheng L

PROVIDER: S-EPMC5228761 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Co-Expression Analysis of Blood Cell Genome Expression to Preliminary Investigation of Regulatory Mechanisms in Uremia.

Cheng Liu L Yonggui Wu W

Medical science monitor : international medical journal of experimental and clinical research 20170104

BACKGROUND Uremia involves a series of clinical manifestations and is a common syndrome that occurs in nearly all end-stage kidney diseases. However, the exact genetic and/or molecular mechanisms that underlie uremia remain poorly understood. MATERIAL AND METHODS In this case-control study, we analyzed whole-genome microarray of 75 uremia patients and 20 healthy controls to investigate changes in gene expression and cellular mechanisms relevant to uremia. Gene co-expression network analysis was ...[more]

PMID: 28050009

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Project description:BackgroundGene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.ResultsWe have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high-grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression. The module without GO enrichment was found to be associated with a potential genomic gain in 8q21-23 in high-grade tumors. The 17-module signature of breast tumor progression clustered patients into subgroups with significantly different relapse-free survival times. Namely, patients with lower cell proliferation and higher cell adhesion levels had significantly lower risk of recurrence, both for all patients (p = 0.004) and for those with grade 2 tumors (p = 0.017).ConclusionsThe ICE algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.

Project description:Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.

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Co-Expression Analysis of Blood Cell Genome Expression to Preliminary Investigation of Regulatory Mechanisms in Uremia.

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Co-Expression Analysis of Blood Cell Genome Expression to Preliminary Investigation of Regulatory Mechanisms in Uremia.

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