Targeting molecular networks for drug research

Pinto, José; Machado, Rui S. R.; Xavier, Joana M.; Futschik, Matthias E.

doi:10.3389/fgene.2014.00160

Cited by 26 publications

(18 citation statements)

References 44 publications

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“…Understanding signaling systems as interacting components can thus be utilized to exploit therapeutic selectivity to the system rather than to single components. For example, targeting a protein complex or pathway rather than an individual protein may give better results if that protein takes on different functions under different conditions 123,124 . A deeper understanding of these protein interactions and pathways will aid us in designing better therapeutics that take into account this complex regulation.…”

Section: Resultsmentioning

confidence: 99%

Surviving apoptosis: life–death signaling in single cells

Flusberg

Sorger

2015

Trends in Cell Biology

130

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Tissue development and homeostasis are regulated by opposing pro-survival and pro-death signals. An interesting feature of the Tumor Necrosis Factor (TNF) family of ligands is that they simultaneously activate opposing signals within a single cell via the same ligand-receptor complex. The magnitude of pro-death events such as caspase activation and pro-survival events such as NF-κB activation vary not only from one cell type to the next but also among individual cells of the same type due to intrinsic and extrinsic noise. The molecules involved in these pro-survival/pro-death pathways, and the different phenotypes that result from their activities, have been recently reviewed. Here we focus on the impact of cell-to-cell variability in the strength of these opposing signals on shaping cell fate decisions.

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Section: Resultsmentioning

confidence: 99%

Surviving apoptosis: life–death signaling in single cells

Flusberg

Sorger

2015

Trends in Cell Biology

130

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“…Prediction of synthetic lethal relationships between genes is another potential network use, for instance Benstead-Hume and colleagues used protein-protein interaction networks to predict human synthetic lethal interactions, which they then confirmed experimentally [Benstead-Hume et al, 2019]. Such analyses could potentially be used to identify targets for new chemotherapies in helminth research [Pinto et al, 2014].…”

Section: Discussionmentioning

confidence: 99%

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

James

Olson

2019

Preprint

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AbstractReference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence. Recent public release of the v.3 assembly of the mouse bile-duct tapeworm, Hymenolepis microstoma, provides chromosome-level characterisation of the genome and a stabilised set of protein coding gene models underpinned by both bioinformatic and empirical data. However, interactome data have not been produced. Conserved protein-protein interactions in other organisms, termed interologs, can be used to transfer interactions between species, allowing systems-level analysis in non-model organisms. Here, we describe a probabilistic, integrated network of interologs for the H. microstoma proteome, based on conserved protein interactions found in eukaryote model species. Almost a third of the 10,139 gene models in the v.3 assembly could be assigned interaction data and assessment of the resulting network indicates that topologically-important proteins are related to essential cellular pathways, and that the network clusters into biologically meaningful components. Moreover, network parameters are similar to those of single-species interaction networks that we constructed in the same way for S. cerevisiae, C. elegans and H. sapiens, demonstrating that information-rich, system-level analyses can be conducted even on species separated by a large phylogenetic distance from the major model organisms from which most protein interaction evidence is based. Using the interolog network, we then focused on sub-networks of interactions assigned to discrete suites of genes of interest, including signalling components and transcription factors, germline ‘multipotency’ genes, and differentially-expressed genes between larval and adult worms. These analyses not only showed an expected bias toward highly-conserved proteins, such as components of intracellular signal transduction, but in some cases predicted interactions with transcription factors that aid in identifying their target genes. With the completion of key helminth genomes, such systems level analyses can provide an important predictive framework to guide basic and applied research on helminths and will become increasingly informative as protein-protein interaction data accumulate.

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“…143 Gene regulatory networks constructed from genome-wide transcriptional profiles and intrinsic genomic variation are able to estimate causal relationships between molecules and identify key drivers of disease. [144][145][146] This new field of network pharmacology is still in the early days 147 but is already illuminating fruitful drug targets for treating diseased states 100,148,149 and producing accurate estimates of off-target effects. 107,109 Alternatively, metabolic models constructed from sets of metabolic reactions can be used to simulate enzyme kinetic activities and perform in silico gene knockouts, which can, for example, help to identify and prioritize new drug targets.…”

Section: Network Methodsmentioning

confidence: 99%

In silico methods for drug repurposing and pharmacology

Hodos

Kidd²,

Shameer³

et al. 2016

WIREs Mechanisms of Disease

286

195

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Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

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Targeting molecular networks for drug research

Cited by 26 publications

References 44 publications

Surviving apoptosis: life–death signaling in single cells

Surviving apoptosis: life–death signaling in single cells

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

In silico methods for drug repurposing and pharmacology

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