An Exploratory Analysis on Drug Target Locality

Abstract—From a network medicine perspective, diseases are caused by perturbations in the dynamics of multiple interacting genes - a disease module. A drug that is a suitable candidate for re-purposing, should affect perturbed disease modules other than the one for which it was designed. In other words, it must act on various disease modules. A systematic analysis of re purposing suitability requires deeper understanding of drug target modularity. In this paper, we present a large-scale analysis of drug-target relationships, evaluating the locality of drug targets in protein-protein interaction networks. We show that the various drugs in each category affect different regions in biological networks, and present modular features. Additionally, multiple targets associated to the same drug appear close in the interactome. Our statistical analysis of the functions of the known drug targets reveals that peripheral functions of disease modules, such as signalling, are common targets for many drugs.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Mining the biomedical literature to predict shared drug targets in drugbank

The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical literature. We show that using MeSH ontology terms can accurately describe the drugs and that appropriate use of the MeSH ontological structure can determine pairwise drug similarity.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Mining the biomedical literature to predict shared drug targets in drugbank

The current drug development pipelines are characterised by long processes with high attrition rates and elevated costs. More than 80% of new compounds fail in the later stages of testing due to severe side-effects caused by unknown biomolecular targets of the compounds. In this work, we present a measure that can predict shared targets for drugs in DrugBank through large scale analysis of the biomedical literature. We show that using MeSH ontology terms can accurately describe the drugs and that appropriate use of the MeSH ontological structure can determine pairwise drug similarity.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Combining Interactomes from Multiple Organisms: a Case Study on Human-Mouse

The amount and quality of available data on different organisms varies greatly. While model organisms benefit from extensive experimental studies, there is often a lack of detailed experimental data for more specific organisms. Additionally, even among model organisms there are noticeable differences in the amount and type of data available, due to the different suitability of experiments in different organisms. The combination of interactomes for closely related species, represents a viable tool to increase the amount of protein- protein interaction data for a given organism. The Human-Mouse case of study is particularly relevant, as many experiments cannot be carried out on humans. This paper describes a general method to construct a combined interactome from different organisms.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

A Recommender System Approach for Predicting Drug Side Effects

The accurate identification of drug side effects represents a major concern for public health. We propose a collaborative filtering model for large-scale prediction of drug side effects. Our approach provides side effects recommendations for drugs to safety professionals. The proposed latent factor model relies solely on the public drug-side effect relationships from safety data.CONACYT – Consejo Nacional de Ciencia y Tecnologí

Progressive promoter element combinations classify conserved orthogonal plant circadian gene expression modules

We aimed to test the proposal that progressive combinations of multiple promoter elements acting in concert may be responsible for the full range of phases observed in plant circadian output genes. In order to allow reliable selection of informative phase groupings of genes for our purpose, intrinsic cyclic patterns of expression were identified using a novel, non-biased method for the identification of circadian genes. Our non-biased approach identified two dominant, inherent orthogonal circadian trends underlying publicly available microarray data from plants maintained under constant conditions. Furthermore, these trends were highly conserved across several plant species. Four phase-specific modules of circadian genes were generated by projection onto these trends and, in order to identify potential combinatorial promoter elements that might classify genes into these groups, we used a Random Forest pipeline which merged data from multiple decision trees to look for the presence of element combinations. We identified a number of regulatory motifs which aggregated into coherent clusters capable of predicting the inclusion of genes within each phase module with very high fidelity and these motif combinations changed in a consistent, progressive manner from one phase module group to the next, providing strong support for our hypothesis

Neurogenomic Signatures of Successes and Failures in Life-History Transitions in a Key Insect Pollinator

Life-history transitions require major reprogramming at the behavioral and physiological level. Mating and reproductive maturation are known to trigger changes in gene transcription in reproductive tissues in a wide range of organisms, but we understand little about the molecular consequences of a failure to mate or become reproductively mature, and it is not clear to what extent these processes trigger neural as well as physiological changes. In this study, we examined the molecular processes underpinning the behavioral changes that accompany the major life-history transitions in a key pollinator, the bumblebee Bombus terrestris. We compared neuro-transcription in queens that succeeded or failed in switching from virgin and immature states, to mated and reproductively mature states. Both successes and failures were associated with distinct molecular profiles, illustrating how development during adulthood triggers distinct molecular profiles within a single caste of a eusocial insect. Failures in both mating and reproductive maturation were explained by a general up-regulation of brain gene transcription. We identified 21 genes that were highly connected in a gene coexpression network analysis: nine genes are involved in neural processes and four are regulators of gene expression. This suggests that negotiating life-history transitions involves significant neural processing and reprogramming, and not just changes in physiology. These findings provide novel insights into basic life-history transitions of an insect. Failure to mate or to become reproductively mature is an overlooked component of variation in natural systems, despite its prevalence in many sexually reproducing organisms, and deserves deeper investigation in the future

Clustering of Pseudomonas aeruginosa transcriptomes from planktonic cultures, developing and mature biofilms reveals distinct expression profiles

BACKGROUND: Pseudomonas aeruginosa is a genetically complex bacterium which can adopt and switch between a free-living or biofilm lifestyle, a versatility that enables it to thrive in many different environments and contributes to its success as a human pathogen. RESULTS: Transcriptomes derived from growth states relevant to the lifestyle of P. aeruginosa were clustered using three different methods (K-means, K-means spectral and hierarchical clustering). The culture conditions used for this study were; biofilms incubated for 8, 14, 24 and 48 hrs, and planktonic culture (logarithmic and stationary phase). This cluster analysis revealed the existence and provided a clear illustration of distinct expression profiles present in the dataset. Moreover, it gave an insight into which genes are up-regulated in planktonic, developing biofilm and confluent biofilm states. In addition, this analysis confirmed the contribution of quorum sensing (QS) and RpoS regulated genes to the biofilm mode of growth, and enabled the identification of a 60.69 Kbp region of the genome associated with stationary phase growth (stationary phase planktonic culture and confluent biofilms). CONCLUSION: This is the first study to use clustering to separate a large P. aeruginosa microarray dataset consisting of transcriptomes obtained from diverse conditions relevant to its growth, into different expression profiles. These distinct expression profiles not only reveal novel aspects of P. aeruginosa gene expression but also provide a growth specific transcriptomic reference dataset for the research community