Network-assisted approaches for human disease research

Shim, Jung Eun; Lee, Insuk

doi:10.1080/19768354.2015.1074108

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…The genetic system of the human opportunistic bacterial pathogen P. aeruginosa is highly complex, which enables P. aeruginosa to be robust and adaptable under many host and drug conditions. Functional gene network models have been utilised to facilitate the genetic dissection of complex traits such as human diseases 49 . Although bacteria are single-celled organisms, screening for their virulence and antibiotic resistance generally uncovers many associated genes.…”

Section: Discussionmentioning

confidence: 99%

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

Hwang

Kim

et al. 2016

Sci Rep

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Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.

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Section: Discussionmentioning

confidence: 99%

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

Hwang

Kim

et al. 2016

Sci Rep

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“…Typical network diffusion methods include random walk with restart , iterative ranking , and Gaussian Smoothing (Wang & Marcotte 2010 ). Information from GWAS and mutation studies can be propagated through co-functional gene networks to prioritize candidate genes for diseases (Shim & Lee 2015 ). For examples, propagated GWAS significance scores from candidate genes through the network were used to identify additional candidate disease genes with low GWAS significance (Lee et al 2011 ), and propagated mutation occurrence scores were used to identify cancer genes with low mutation occurrences among patients (Cho et al 2016 ).…”

Section: Generating Functional Hypotheses From Co-functional Networkmentioning

confidence: 99%

From sequencing data to gene functions: co-functional network approaches

Shim

Lee

2017

Animal Cells and Systems

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Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis is a legitimate way to study gene functions. The workflow of network-based functional study is composed of three steps: (i) inferencing co-functional links, (ii) evaluating and integrating the links into genome-scale networks, and (iii) generating functional hypotheses from the networks. Co-functional links can be inferred from DNA sequencing data by using phylogenetic profiling, gene neighborhood, domain profiling, associalogs, and co-expression analysis from RNA sequencing data. The inferred links are then evaluated and integrated into a genome-scale network with aid from gold-standard co-functional links. Functional hypotheses can be generated from the network based on (i) network connectivity, (ii) network propagation, and (iii) subnetwork analysis. The functional analysis pipeline described here requires only sequencing data which can be readily available for most species by next-generation sequencing technology. Therefore, co-functional networks will greatly potentiate the use of the sequencing data for the study of genetics in any cellular organism.

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“…MouseNet v2 can prioritize candidate genes for a pathway/trait of interest. The study of complex traits such as polygenic diseases can be facilitated by network analysis, because genes for a phenotype or disease tend to be functionally associated ( 25 ). Thus, we implemented pathway-centric network search algorithm, in which known genes for a pathway/trait provided by the user guide the search for new candidates in the network.…”

Section: Assessment and Applicationsmentioning

confidence: 99%

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates

et al. 2015

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Laboratory mouse, Mus musculus, is one of the most important animal tools in biomedical research. Functional characterization of the mouse genes, hence, has been a long-standing goal in mammalian and human genetics. Although large-scale knockout phenotyping is under progress by international collaborative efforts, a large portion of mouse genome is still poorly characterized for cellular functions and associations with disease phenotypes. A genome-scale functional network of mouse genes, MouseNet, was previously developed in context of MouseFunc competition, which allowed only limited input data for network inferences. Here, we present an improved mouse co-functional network, MouseNet v2 (available at http://www.inetbio.org/mousenet), which covers 17 714 genes (>88% of coding genome) with 788 080 links, along with a companion web server for network-assisted functional hypothesis generation. The network database has been substantially improved by large expansion of genomics data. For example, MouseNet v2 database contains 183 co-expression networks inferred from 8154 public microarray samples. We demonstrated that MouseNet v2 is predictive for mammalian phenotypes as well as human diseases, which suggests its usefulness in discovery of novel disease genes and dissection of disease pathways. Furthermore, MouseNet v2 database provides functional networks for eight other vertebrate models used in various research fields.

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Network-assisted approaches for human disease research

Cited by 13 publications

References 23 publications

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa

From sequencing data to gene functions: co-functional network approaches

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates

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