Characterization of essential genes by topological properties in the perturbation sensitivity network

Topological Analysis of Regulatory Networks Reveals Functionally Key Genes and miRNAs Involved in the Differentiation of Mesenchymal Stem Cells

Background

The details of molecular mechanisms underlying the differentiation of Mesenchymal Stem Cells (MSCs) into specific lineages are not well understood.

Objectives

We aimed to construct the interactome network and topology analysis of bone marrow mesenchymal stem cell of CAGE data. Applying the enrichment results, we wanted to introduce the common genes and hub-microRNA and hub-genes of these giant network.

Materials and Methods

In this study, we constructed gene regulatory networks for each non-mesenchymal cell lineage according to their gene expression profiles obtained from FANTOM5 database. The putative interactions of TF-gene and protein-protein were determined using TRED, STRING, HPRD and GeneMANIA servers. In parallel, a regulatory network including corresponding miRNAs and total differentially expressed genes (DEGs) was constructed for each cell lineage.

Results

The results indicated that analysis of networks' topology can significantly distinguish the hub regulatory genes and miRNAs involved in the differentiation of MSCs. The functional annotation of identified hub genes and miRNAs revealed that several signal transduction pathways i.e. AKT, WNT and TGFβ and cell proliferation related pathways play a pivotal role in the regulation of MSCs differentiation. We also classified cell lineages into two groups based on their predicted miRNA profiles.

Conclusions

In conclusion, we found a number of hub genes and miRNAs which seem to have key regulatory functions during differentiation of MSCs. Our results also introduce a number of new regulatory genes and miRNAs which can be considered as the new candidates for genetic manipulation of MSCs in vitro.

Characterization of human proteins with different subcellular localizations by topological and biological properties

Knowing the protein localization can provide valuable information resource for elucidating protein function. In recent years, with the advances of human genomics and proteomics, it is possible to characterize human proteins that are located in different subcellular localizations. In this study, we used the topological properties and biological properties to characterize human proteins with six subcellular localizations. Almost all of these properties were found to be significantly different among six protein categories. Network topology analysis indicated that several significant topological properties, including the degree and k-core, were higher for the mitochondrial proteins. Biological property analysis showed that the nuclear proteins appeared to be correlated with important biological function. We hope these findings may provide some important help for comprehensive understanding the biological function of proteins, and prediction of protein subcellular localizations in human.

Species-specific transcriptomic network inference of interspecies interactions

The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.

Network-Oriented Approaches to Anticancer Drug Response

A long-standing paradigm in drug discovery has been the concept of designing maximally selective drugs to act on individual targets considered to underlie a disease of interest. Nonetheless, although some drugs have proven to be successful, many more potential drugs identified by the "one gene, one drug, one disease" approach have been found to be less effective than expected or to cause notable side effects. Advances in systems biology and high-throughput in-depth genomic profiling technologies along with an analysis of the successful and failed drugs uncovered that the prominent factor to determine drug sensitivity is the intrinsic robustness of the response of biological systems in the face of perturbations. The complexity of the molecular and cellular bases of systems responses to drug interventions has fostered an increased interest in systems-oriented approaches to drug discovery. Consonant with this knowledge of the multifactorial mechanistic basis of drug sensitivity and resistance is the application of network-based approaches for the identification of molecular (multi-)feature signatures associated with desired (multi-)drug phenotypic profiles. This chapter illustrates the principal network analysis and inference techniques which have found application in systems-oriented drug design and considers their benefits and drawbacks in relation to the nature of the data produced by network pharmacology.

Gene essentiality prediction based on fractal features and machine learning

Predicting bacterial essential genes using only fractal features.

Genome-wide characterization of essential, toxicity-modulating and no-phenotype genes in S. cerevisiae

Based on the requirements for an organism's viability, genes can be classified into essential genes and non-essential genes. Non-essential genes can be further classified into toxicity-modulating genes and no-phenotype genes based on the fitness phenotype of yeast cells when the gene is deleted under DNA-damaging conditions. In this study, graph theoretical approaches were used to characterize essential, toxicity-modulating and no-phenotype genes for S. cerevisiae in the physical interaction (PI) network and the perturbation sensitivity (PS) network. We also gained previously published biological datasets to gain a more complete understanding of the differences and relationships between essential, toxicity-modulating genes and no-phenotype genes. The analysis results indicate that toxicity-modulating genes have similar properties as essential genes, and toxicity-modulating genes might represent a middle ground between essential genes and no-phenotype genes, suggesting that cells initiate highly coordinated responses to damage that are similar to those needed for vital cellular functions. These findings may elucidate the mechanisms for understanding toxicity-modulating processes relevant to certain diseases.

Characterization of proteins in S. cerevisiae with subcellular localizations

Acquiring comprehensive knowledge of protein in various subcellular localizations is one of the fundamental goals in cell biology and proteomics.