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Background: Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results: Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion: The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive, degenerative neurological disease, typically characterized by tremors and muscle rigidity. The present study aimed to identify differentially expressed genes (DEGs) between patients with PD and healthy patients, and clarify their association with additional biological processes that may regulate factors that lead to PD. An integrated analysis of publicly available Gene Expression Omnibus datasets of PD was performed. DEGs were identified between PD and normal blood samples. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as protein‑protein interaction (PPI) networks were used to predict the functions of identified DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was performed to validate the predicted expression levels of identified DEGs in whole blood samples obtained from patients with PD and normal healthy controls. A total of 292DEGs were identified between the PD and normal blood samples. Of these, 156 genes were significantly upregulated and 136 genes were significantly downregulated in PD samples following integrated analysis of four PD expression datasets. The 10 most upregulated and downregulated genes were used to construct a PPI network, where ubiquitin C‑terminal hydrolase L1 (UCHL1), 3‑phosphoinositide dependent protein kinase 1 (PDPK1) and protein kinase cAMP‑activated catalytic subunit β (PRKACB) demonstrated the highest connectivity in the network. DEGs were significantly enriched in amoebiasis, vascular smooth muscle contraction, and the Wnt and calcium signaling pathways. The expression levels of significant DEGs, UCHL1, PDPK1 and PRKACB were validated using RT‑qPCR analysis. The findings revealed that UCHL1 and PDPK1 were upregulated and PRKACB was downregulated in patients with PD when compared with normal healthy controls. In conclusion, the results indicate that the significant DEGs, including UCHL1, PDPK1 and PRKACB may be associated with the development of PD. In addition, these factors may be involved in various signaling pathways, including amoebiasis, vascular smooth muscle contraction and the Wnt and calcium signaling pathways. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ß-cells require a constant supply of zinc to maintain normal insulin secretory function. Following co-exocytosis with insulin, zinc is replenished via the Zrt- and Irt-like (ZIP; SLC39A) family of transporters. However the ZIP paralogues of particular importance for zinc uptake, and associations with ß-cell function and Type 2 Diabetes remain largely unexplored. We retrieved and statistically analysed publically available microarray and RNA-seq datasets to perform a systematic review on the expression of ß-cell SLC39A paralogues. We complemented results with experimental data on expression profiling of human islets and mouse ß-cell derived MIN6 cells, and compared transcriptomic and proteomic sequence conservation between human, mouse and rat. Results: The 14 ZIP paralogues have 73-98% amino sequence conservation between human and rodents. We identified 18 datasets for â-cell SLC39A analysis, which compared relative expression to non-â-cells, and expression in response to PDX-1 activity, cytokines, glucose and type 2 diabetic status. Published expression data demonstrate enrichment of transcripts for ZIP7 and ZIP9 transporters within rodent â-cells and of ZIP6, ZIP7 and ZIP14 within human â-cells, with ZIP1 most differentially expressed in response to cytokines and PDX-1 within rodent, and ZIP6 in response to diabetic status in human and glucose in rat. Our qPCR expression profiling data indicate that SLC39A6, .9, .13, and .14 are the highest expressed paralogues in human â-cells and Slc39a6 and .7 in MIN6 cells. Conclusions: Our systematic review, expression profiling and sequence alignment reveal similarities and potentially important differences in ZIP complements between human and rodent ß-cells. We identify ZIP6, ZIP7, ZIP9, ZIP13 and ZIP14 in human and rodent and ZIP1 in rodent as potentially biologically important for ß-cell zinc trafficking. We propose ZIP6 and ZIP7 are key functional orthologues in human and rodent ß-cells and highlight these zinc importers as important targets for exploring associations between zinc status and normal physiology of ß-cells and their decline in Type 2 Diabetes. [ABSTRACT FROM AUTHOR]

Biclustering represents an intrinsically complex problem, where the aim is to perform a simultaneous row- and column-clustering of a given data matrix. Some recent approaches model this problem using factor graphs, so to exploit their ability to open the door to efficient optimization approaches for well designed function decompositions. However, while such models provide promising results, they do not scale to data matrices of reasonable size. In this paper, we take a step towards addressing this issue, by proposing a novel approach to biclustering based on factor graphs, which yields high quality solutions and scales more favorably than previous methods. Specifically, we cast biclustering as the sequential search for a single bicluster, and propose a binary and compact factor graph that can be solved efficiently using the max-sum algorithm. The proposed approach has been tested and compared with state-of-the-art methods on four datasets (two synthetic and two real world data), providing encouraging results with respect both to previous approaches based on factor graphs and to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations. Results: In this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver). Conclusions: Experiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complex neurodegenerative disorder with largely unknown genetic mechanisms. Identifying altered neuronal gene expression in AD may provide diagnostic or therapeutic targets for AD. The present study aimed to identify differentially expressed genes (DEGs) and their further association with other biological processes that regulate causative factors for AD. The present study performed an integrated analysis of publicly available gene expression omnibus datasets of AD hippocampi. Gene ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) network analysis were performed. The present study detected 295 DEGs (109 upregulated and 186 downregulated genes) in hippocampi between AD and control samples by integrating four datasets of gene expression profiles of hippocampi of patients with AD. Respiratory electron transport chain (GO: 0022904; P=1.64×10-11) was the most significantly enriched GO term among biological processes, while for molecular functions, the most significantly enriched GO term was that of protein binding (GO: 0005515; P=3.03×10-29), and for cellular components, the most significantly enriched GO term was that of the cytoplasm (GO: 0005737; P=8.67×10-33). The most significant pathway in the KEGG analysis was oxidative phosphorylation (P=1.61×10--13). PPI network analysis showed that the significant hub proteins contained β-actin (degree, 268), hepatoma-derived growth factor (degree, 218) and WD repeat-containing protein 82 (degree, 87). The integrated analysis performed in the present study serves as a basis for identifying novel drug targets to develop improved therapies and interventions for common and devastating neurological diseases such as AD. [ABSTRACT FROM AUTHOR]

The human Y chromosome contains a small number of genes that play a critical role in the determination of male-specific organs. Today's advances have provided valuable resources for defining the functions of this chromosome in both normal and cancerous prostates. Despite the fact that generation of high-throughput expression data is becoming usual; the systematic methods of data analysis in a biological context are still an impediment. Here we have shown that constructing co-expression networks using Y-chromosome genes provides an alternative strategy for the detection of new candidate genes involved in prostate cancer. In our approach, independent co-expression networks from normal and cancerous stages are reconstructed using a reverse engineering approach. We then highlight crucial pathways, biological processes, and genes involved in the prostate cancer by analyzing each network individually and in concert. Thus, we have identified 18 critical pathways and processes related to prostate cancer, many of which have previously been shown to be involved in cancer. In particular, we identify 2 2 Y-chromosome genes putatively linked to prostate cancer, 13 of which have been already verified experimentally. Our novel network-based approach is useful for accurate inference of processes and essential regulators that mediate molecular changes during cancer progression. [ABSTRACT FROM AUTHOR]

Background: To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. Methods: We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) networks analysis. Results: Eight GEO datasets, including 240 samples of OS and 35 samples of controls, were available for the meta-analysis. We identified 979 DEGs across the studies between OS and NC tissues (472 up-regulated and 507 down-regulated). We found GO terms for molecular functions significantly enriched in protein binding (GO: 0005515, P = 3.83E-60) and calcium ion binding (GO: 0005509, P = 3.79E-13), while for biological processes, the enriched GO terms were cell adhesion (GO:0007155, P = 2.26E-19) and negative regulation of apoptotic process (GO: 0043066, P = 3.24E-15), and for cellular component, the enriched GO terms were cytoplasm (GO: 0005737, P = 9.18E-63) and extracellular region (GO: 0005576, P = 2.28E-47). The most significant pathway in our KEGG analysis was Focal adhesion (P = 5.70E-15). Furthermore, ECM-receptor interaction (P = 1.27E-13) and Cell cycle (P = 4.53E-11) are found to be highly enriched. PPI network analysis indicated that the significant hub proteins containing PTBP2 (Degree = 33), RGS4 (Degree = 15) and FXYD6 (Degree = 13). Conclusions: Our meta-analysis detected DEGs and biological functions associated with gene expression changes between OS and NC tissues, guiding further identification and treatment for OS. [ABSTRACT FROM AUTHOR]

Abstract: Integrating biological information from different sources to understand cellular processes is an important problem in systems biology. We use data from mRNA expression arrays and chemical kinetics to formulate a metabolic model relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters the expression of metabolic enzymes in K562 cells. Our array data show changes in expression of lactate dehydrogenase (LDH) isoforms after treatment with phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We model the change in lactate production which occurs when the MAP kinase pathway is activated, using a non-equilibrium, chemical-kinetic model of homolactic fermentation. In particular, we examine the role of LDH isoforms, which catalyse the conversion of pyruvate to lactate. Changes in the isoform ratio are not the primary determinant of the production of lactate. Rather, the total concentration of LDH controls the lactate concentration. [Copyright &y& Elsevier]

Abstract: Most studies concerning expression data analyses usually exploit information on the variability of gene intensity across samples. This information is sensitive to initial data processing, which affects the final conclusions. However expression data contains scale-free information, which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for a classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results. [Copyright &y& Elsevier]

Homocracy, a term referring to shared regulatory gene expression patterns between organs in different animals, was introduced recently in order to prevent inappropriate inference of organ homology based on gene expression data. Non-homologous structures expressing homologous genes, and homologous structures expressing non-homologous genes illustrate that gene expression data is not sufficient on its own to identify morphological homology. However, gene expression data might be useful in testing hypotheses of organ homology, because parsimony can be applied on changes in the relation between expression of orthologous regulatory genes and the formation of homologous organs. A method of testing organ homology hypotheses with respect to change in regulatory gene expression required within a particular phylogenetic context is presented. [ABSTRACT FROM AUTHOR]

Large-scale expression data are today measured for thousands of genes simultaneously. This development has been followed by an exploration of theoretical tools to get as much information out of these data as possible. Several groups have used principal component analysis (PCA) for this task. However, since this approach is data-driven, care must be taken in order not to analyze the noise instead of the data. As a strong warning towards uncritical use of the output from a PCA, we employ a newly developed procedure to judge the effective dimensionality of a specific data set. Although this data set is obtained during the development of rat central nervous system, our finding is a general property of noisy time series data. Based on knowledge of the noise-level for the data, we find that the effective number of dimensions that are meaningful to use in a PCA is much lower than what could be expected from the number of measurements. We attribute this fact both to effects of noise and the lack of independence of the expression levels. Finally, we explore the possibility to increase the dimensionality by performing more measurements within one time series, and conclude that this is not a fruitful approach. [Copyright &y& Elsevier]

The identification of regulatory elements in silico is an important method for inferring function from sequence data, but it is uncertain which methods are best. We used a novel combination of expression data from a TCF1 knockout mouse (TCF1 codes for the transcription factor HNF1α), and human and mouse genome sequences, to search 2kb upstream of 28 genes downregulated in TCF1 null mice compared to wild type mice. We wrote software (http://www.BindGene.org) to search for and assign p-values to potential binding sites. This identified 8 genes as candidates for being directly regulated by HNF1α: LIPC, CRP, F13B, PRODH2, HSD17B2, SCL7A9, SLC16A7, PAH. There was evidence for conservation between human and mouse for all these regions identified as containing putative binding sites. For three of the genes identified there was experimental evidence for an HNF1α binding site. For comparison we also examined 25 genes up-regulated in TCF1 null mice; only one gene was selected and there was little evidence for conservation of this putative binding site between human and mouse. This result was consistent with HNF1α being a gene transcription activator. Another 6 up-regulated genes had unexpectedly high p-values, suggesting that possibly HNF1α sites have been suppressed from these genes. In conclusion, gene expression data from transgenic animals lacking a transcription factor can be used to identify DNA binding sites for that factor. [ABSTRACT FROM AUTHOR]

Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or 'regimes', which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes. Reviewers: This article was reviewed by Dirk Walther, Francisco Garcia and Isabel Nepomuceno. [ABSTRACT FROM AUTHOR]