Deconvoluting essential gene signatures for cancer growth from genomic expression in compound-treated cells.

MOTIVATION: Essential gene signatures for cancer growth have been typically identified via RNAi or CRISPR-Cas9. Here, we propose an alternative method that reveals the essential gene signatures by analysing genomic expression profiles in compound-treated cells. With a large amount of the existing compound-induced data, essential gene signatures at genomic scale are efficiently characterized without technical challenges in the previous techniques. RESULTS: An essential gene is characterized as a gene presenting positive correlation between its down-regulation and cell growth inhibition induced by diverse compounds, which were collected from LINCS and CGP. Among 12 741 genes, 1092, 1 228 827 962, 1 664 580 and 829 essential genes are characterized for each of A375, A549, BT20, LNCAP, MCF7, MDAMB231 and PC3 cell lines (P-value ≤ 1.0E-05). Comparisons to the previously identified essential genes yield significant overlaps in A375 and A549 (P-value ≤ 5.0E-05) and the 103 common essential genes are enriched in crucial processes for cancer growth. In most comparisons in A375, MCF7, BT20 and A549, the characterized essential genes yield more essential characteristics than those of the previous techniques, i.e. high gene expression, high degrees of protein-protein interactions, many homologs and few paralogs. Remarkably, the essential genes commonly characterized by both the previous and proposed techniques show more significant essential characteristics than those solely relied on the previous techniques. We expect that this work provides new aspects in essential gene signatures. AVAILABILITY AND IMPLEMENTATION: The Python implementations are available at https://github.com/jmjung83/deconvolution_of_essential_gene_signitures. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

BACKGROUND: Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins. RESULTS: We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research. CONCLUSIONS: We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.

Publisher Correction: CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Author Correction: CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

HIDEEP: a systems approach to predict hormone impacts on drug efficacy based on effect paths.

Experimental evidence has shown that some of the human endogenous hormones significantly affect drug efficacy. Since hormone status varies with individual physiological states, it is essential to understand the interplay of hormones and drugs for precision medicine. Here, we developed an in silico method to predict interactions between 283 human endogenous hormones and 590 drugs for 20 diseases including cancers and non-cancer diseases. We extracted hormone effect paths and drug effect paths from a large-scale molecular network that contains protein interactions, transcriptional regulations, and signaling interactions. If two kinds of effect paths for a hormone-drug pair intersect closely, we expect that the influence of the hormone on the drug efficacy is significant. It has been shown that the proposed method correctly distinguishes hormone-drug pairs with known interactions from random pairs in blind experiments. In addition, the method can suggest underlying interaction mechanisms at the molecular level so that it helps us to better understand the interplay of hormones and drugs.

CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects.

In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.

Prediction of drugs having opposite effects on disease genes in a directed network.

BACKGROUND: Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information. METHODS: We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with 'effect type', an integrated directed molecular network with 'effect type' and 'effect direction', and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations. RESULTS: We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main . CONCLUSIONS: Our results showed that 'effect type' and 'effect direction' information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.

Context-based resolution of semantic conflicts in biological pathways.

BACKGROUND: Interactions between biological entities such as genes, proteins and metabolites, so called pathways, are key features to understand molecular mechanisms of life. As pathway information is being accumulated rapidly through various knowledge resources, there are growing interests in maintaining the integrity of the heterogeneous databases. METHODS: Here, we defined conflict as a status where two contradictory pieces of evidence (i.e. 'A increases B' and 'A decreases B') coexist in a same pathway. This conflict damages unity so that inference of simulation on the integrated pathway network might be unreliable. We defined rule and rule group. A rule consists of interaction of two entities, meta-relation (increase or decrease), and contexts terms about tissue specificity or environmental conditions. The rules, which have the same interaction, are grouped into a rule group. If the rules don't have a unanimous meta-relation, the rule group and the rules are judged as being conflicting. RESULTS: This analysis revealed that almost 20% of known interactions suffer from conflicting information and conflicting information occurred much more frequently in the literature than the public database. CONCLUSIONS: By identifying and resolving the conflicts, we expect that pathway databases can be cleaned and used for better secondary analyses such as gene/protein annotation, network dynamics and qualitative/quantitative simulation.