- MeSH
- Antifungal Agents metabolism MeSH
- Research Support as Topic MeSH
- Genes, Fungal physiology MeSH
- Yeasts physiology drug effects MeSH
- Membrane Proteins physiology MeSH
- Drug Resistance, Multiple genetics MeSH
- Fungicides, Industrial metabolism MeSH
- Transcription Factors physiology MeSH
- Publication type
- Review MeSH
We examine the modular structure of the metabolic network when combined with the regulatory network representing direct regulation of enzymes by small metabolites in E. coli. We introduce novel clustering algorithm and compare it with mainstream module detection method based on simulated annealing. Both methods identify the similar modular core. Slight but significant increase in modularity is observed after regulatory interactions addition. We also identify new functional modules in the combined network, which cannot be detected in the metabolic network only. Regulatory loops in the modules are the source of their autonomy, and allow us to hypothesise about module function.
This study describes the meta-analysis and kinetic modelling of gene expression control by sigma factor SigA of Bacillus subtilis during germination and outgrowth based on microarray data from 14 time points. The analysis computationally models the direct interaction among SigA, SigA-controlled sigma factor genes (sigM, sigH, sigD, sigX), and their target genes. Of the >800 known genes in the SigA regulon, as extracted from databases, 311 genes were analysed, and 190 were confirmed by the kinetic model as being controlled by SigA. For the remaining genes, alternative regulators satisfying kinetic constraints were suggested. The kinetic analysis suggested another 214 genes as potential SigA targets. The modelling was able to (i) create a particular SigA-controlled gene expression network that is active under the conditions for which the expression time series was obtained, and where SigA is the dominant regulator, (ii) suggest new potential SigA target genes, and (iii) find other possible regulators of a given gene or suggest a new mechanism of its control by identifying a matching profile of unknown regulator(s). Selected predicted regulatory interactions were experimentally tested, thus validating the model.
- MeSH
- Bacillus subtilis genetics MeSH
- Bacterial Proteins genetics MeSH
- Transcription, Genetic genetics MeSH
- Gene Regulatory Networks genetics MeSH
- Kinetics MeSH
- Gene Expression Regulation, Bacterial genetics MeSH
- Sigma Factor genetics MeSH
- Spores, Bacterial genetics MeSH
- Transcription Factors genetics MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
- Research Support, Non-U.S. Gov't MeSH
... and Evolution -- The Regulatory Apparatus Encoded in the DNA 2 -- The Genes and Gene Regulatory Components ... ... of Animal -- Genomes 2 -- Overview of Regulatory Architecture 7 -- Gene Regulatory Functions in Development ... ... -- Genomic Regulatory Sequence and the Evolution of -- Morphological Features 18 -- Regulatory Evolution ... ... Inside the cis -Regulatory Module: Control Logic, and -- How Regulatory Environment is Transduced into ... ... of Type 1 Regulatory Networks 101 -- 4. ...
1st ed. xii, 261 s.
Formation of a dorsoventral axis is a key event in the early development of most animal embryos. It is well established that bone morphogenetic proteins (Bmps) and Wnts are key mediators of dorsoventral patterning in vertebrates. In the cephalochordate amphioxus, genes encoding Bmps and transcription factors downstream of Bmp signaling such as Vent are expressed in patterns reminiscent of those of their vertebrate orthologues. However, the key question is whether the conservation of expression patterns of network constituents implies conservation of functional network interactions, and if so, how an increased functional complexity can evolve. Using heterologous systems, namely by reporter gene assays in mammalian cell lines and by transgenesis in medaka fish, we have compared the gene regulatory network implicated in dorsoventral patterning of the basal chordate amphioxus and vertebrates. We found that Bmp but not canonical Wnt signaling regulates promoters of genes encoding homeodomain proteins AmphiVent1 and AmphiVent2. Furthermore, AmphiVent1 and AmphiVent2 promoters appear to be correctly regulated in the context of a vertebrate embryo. Finally, we show that AmphiVent1 is able to directly repress promoters of AmphiGoosecoid and AmphiChordin genes. Repression of genes encoding dorsal-specific signaling molecule Chordin and transcription factor Goosecoid by Xenopus and zebrafish Vent genes represents a key regulatory interaction during vertebrate axis formation. Our data indicate high evolutionary conservation of a core Bmp-triggered gene regulatory network for dorsoventral patterning in chordates and suggest that co-option of the canonical Wnt signaling pathway for dorsoventral patterning in vertebrates represents one of the innovations through which an increased morphological complexity of vertebrate embryo is achieved.
- MeSH
- 5' Untranslated Regions MeSH
- Chordata genetics MeSH
- Zebrafish embryology genetics MeSH
- Embryo, Nonmammalian MeSH
- Phylogeny MeSH
- Genetic Variation genetics physiology MeSH
- Gene Regulatory Networks MeSH
- Homeodomain Proteins genetics MeSH
- Conserved Sequence genetics MeSH
- Cells, Cultured MeSH
- Humans MeSH
- Evolution, Molecular MeSH
- Molecular Sequence Data MeSH
- Oryzias embryology genetics MeSH
- Goosecoid Protein genetics MeSH
- Body Patterning genetics MeSH
- Amino Acid Sequence MeSH
- Base Sequence MeSH
- Sequence Homology, Amino Acid MeSH
- Gene Expression Regulation, Developmental MeSH
- Xenopus laevis embryology genetics MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Oral squamous cell carcinoma (OSCC) severely affects the quality of life and the 5-year survival rate is low. Exploring the potential miRNA-mRNA regulatory network and analyzing hub genes and clinical data can provide a theoretical basis for further elucidating the pathogenesis of OSCC. METHODS: The miRNA expression datasets of GSE113956 and GSE124566 and mRNA expression datasets of GSE31056, GSE37991 and GSE13601 were obtained from the Gene Expression Omnibus databases. The differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were screened using GEO2R. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. The PPI network was established through STRING database and the hub genes were preliminarily screened out by Cytoscape software. After identifying the hub genes in the TCGA database, we predicted the potential DEM transcription factors, constructed a miRNA-mRNA regulatory network, and analyzed the relationship between the hub genes and clinical data. RESULTS: A total of 28 DEMs and 764 DEGs were screened out, which were composed of 285 up-regulated genes and 479 down-regulated genes. Enrichment analysis showed that up-regulation of DEGs were mainly enriched in extracellular matrix organization and cancer-related pathway, while down-regulation of DEGs were mainly enriched in muscular system process and adrenaline signal transduction. After preliminary screening by PPI network and identification in TCGA, the up-regulated FN1, COL1A1, COL1A2, AURKA, CCNB1, CCNA2, SPP1, CDC6, and down-regulated ACTN2, TTN, IGF1, CAV3, MYL2, DMD, LDB3, CSRP3, ACTA1, PPARG were identified as hub genes. The miRNA-mRNA regulation network showed that hsa-miR-513b was the DEM with the most regulation, and COL1A1 was the DEG with the most regulation. In addition, CDC6, AURKA, CCNB1 and CCNA2 were related to overall survival and tumor differentiation. CONCLUSIONS: The regulatory relationship of hsa-miR-513b/ CDC6, CCNB1, CCNA2 and the regulatory relationship of hsa-miR-342-5p /AURKA were not only verified in the miRNA-mRNA regulatory network but also related to overall survival and tumor differentiation. These results indicated that they participated in the cellular regulatory process, and provided a molecular mechanism model for the study of pathogenesis.
- MeSH
- Epinephrine MeSH
- Aurora Kinase A genetics metabolism MeSH
- Squamous Cell Carcinoma of Head and Neck * genetics MeSH
- Gene Regulatory Networks MeSH
- Quality of Life MeSH
- Humans MeSH
- RNA, Messenger genetics metabolism MeSH
- MicroRNAs * genetics MeSH
- Mouth Neoplasms * genetics MeSH
- PPAR gamma genetics metabolism MeSH
- Gene Expression Regulation, Neoplastic MeSH
- Gene Expression Profiling MeSH
- Transcription Factors genetics MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 genes. One such method for the gene expression inference is a D-GEX which employs neural networks. RESULTS: We propose two novel D-GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture - a checkerboard architecture with a skip connection and five towers - together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128. CONCLUSIONS: Our proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.
BACKGROUND: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. RESULTS: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. CONCLUSIONS: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.
- MeSH
- Algorithms MeSH
- Bayes Theorem MeSH
- Gene Regulatory Networks MeSH
- Humans MeSH
- Colonic Neoplasms * MeSH
- Systems Biology methods MeSH
- DNA Copy Number Variations * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
