| 1. |
- Chavali, Sreenivas, et al.
(författare)
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MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases
- 2013
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Ingår i: Rna-a Publication of the Rna Society. - : Cold Spring Harbor Laboratory. - 1355-8382 .- 1469-9001. ; 19:11, s. 1552-1562
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Tidskriftsartikel (refereegranskat)abstract
- MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions.
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| 2. |
- Chavali, Sreenivas, et al.
(författare)
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Network properties of human disease genes with pleiotropic effects
- 2010
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Ingår i: BMC Systems Biology. - 1752-0509. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- The phenotypic consequence of a human disease gene is largely affected by the topological position of its protein product in the molecular interaction network. Here, we investigated the differences in properties of specific human disease genes that are associated with one phenotype and shared genes with pleiotropic effects in the context of molecular interaction networks. We find that the shared genes have an intermediate centrality between essential and specific genes. Shared genes causing phenotypically divergent diseases (phenodiv genes) are more central to those causing phenotypically similar diseases (phenosim genes). Shared genes had higher number of disease gene interactors compared to specific genes, implying a higher likelihood of finding a novel disease gene in the network neighborhood of shared genes. Specific genes are more co-expressed with their interactors than shared genes. Relatively restricted tissue co-expression with interactors appears to be a function of shared genes leading to pleiotropy. We demonstrate essential and phenodiv genes with comparable connectivities (degrees) are intra-modular and inter-modular hubs with the former highly co-expressed with their interactors contrary to the phenodiv genes. Essential genes are predominantly nuclear proteins with transcriptional regulator activities while phenodiv genes are cytoplasmic proteins involved in signal transduction. Our results demonstrate that the ability of a disease gene to influence the cellular network determines its role in manifesting different and divergent diseases.
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| 3. |
- Pedicini, Marco, et al.
(författare)
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Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation.
- 2010
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Ingår i: PLoS computational biology. - : Public Library of Science (PLoS). - 1553-7358 .- 1553-734X. ; 6:12
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Tidskriftsartikel (refereegranskat)abstract
- Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.
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