A Systems Level Comparison of Mycobacterium tuberculosis, Mycobacterium leprae and Mycobacterium smegmatis Based on Functional Interaction Network Analysis

Abstract Background Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. Methods We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. Results A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. Conclusions Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

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Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

Advances in Bioinformatics ◽

10.1155/2011/801478 ◽

2011 ◽

Vol 2011 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Gaston K. Mazandu ◽

Nicola J. Mulder

Keyword(s):

Mycobacterium Tuberculosis ◽

Drug Targets ◽

Large Scale ◽

Global Analysis ◽

Interaction Network ◽

Interaction Networks ◽

Microbial Pathogens ◽

Potential Drug ◽

Functional Interaction ◽

Potential Drug Targets

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

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Functional Interaction Network Construction and Analysis for Disease Discovery

Protein Bioinformatics - Methods in Molecular Biology ◽

10.1007/978-1-4939-6783-4_11 ◽

2017 ◽

pp. 235-253 ◽

Cited By ~ 17

Author(s):

Guanming Wu ◽

Robin Haw

Keyword(s):

Interaction Network ◽

Functional Interaction ◽

Network Construction ◽

Functional Interaction Network

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Functional Interaction Network

Encyclopedia of Systems Biology ◽

10.1007/978-1-4419-9863-7_100527 ◽

2013 ◽

pp. 772-772

Keyword(s):

Interaction Network ◽

Functional Interaction ◽

Functional Interaction Network

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Clustering and Cross-talk in a Yeast Functional Interaction Network

2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology ◽

10.1109/cibcb.2006.330983 ◽

2006 ◽

Cited By ~ 2

Author(s):

Jennifer Hallinan ◽

Anil Wipat

Keyword(s):

Cross Talk ◽

Interaction Network ◽

Functional Interaction ◽

Functional Interaction Network

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Proteome and acylome analyses of the functional interaction network between the carbazole-degradative plasmid pCAR1 and host Pseudomonas putida KT2440

Environmental Microbiology Reports ◽

10.1111/1758-2229.12639 ◽

2018 ◽

Vol 10 (3) ◽

pp. 299-309 ◽

Cited By ~ 4

Author(s):

Delyana Vasileva ◽

Chiho Suzuki-Minakuchi ◽

Saori Kosono ◽

Minoru Yoshida ◽

Kazunori Okada ◽

...

Keyword(s):

Pseudomonas Putida ◽

Interaction Network ◽

Functional Interaction ◽

Pseudomonas Putida Kt2440 ◽

Functional Interaction Network ◽

Degradative Plasmid

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Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

F1000Research ◽

10.12688/f1000research.19592.1 ◽

2019 ◽

Vol 8 ◽

pp. 908 ◽

Cited By ~ 2

Author(s):

Aurora S. Blucher ◽

Shannon K. McWeeney ◽

Lincoln Stein ◽

Guanming Wu

Keyword(s):

Targeted Therapies ◽

Biological Networks ◽

Drug Target ◽

Drug Targets ◽

Interaction Network ◽

Genetic Alterations ◽

Patient Specific ◽

Functional Interaction ◽

Functional Interaction Network ◽

A Genome

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

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Characterization of a Mycobacterium smegmatis Mutant Lacking Penicillin Binding Protein 1

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.43.12.3011 ◽

1999 ◽

Vol 43 (12) ◽

pp. 3011-3013 ◽

Cited By ~ 26

Author(s):

Helen Billman-Jacobe ◽

Ruth E. Haites ◽

Ross L. Coppel

Keyword(s):

Mycobacterium Tuberculosis ◽

Mycobacterium Smegmatis ◽

Liquid Culture ◽

Binding Protein ◽

Mycobacterium Leprae ◽

Deficient Mutant ◽

Penicillin Binding Protein

ABSTRACT The ponA gene of Mycobacterium smegmatisencodes a 95-kDa penicillin binding protein, PBP1, that is similar to PBP1s of Mycobacterium tuberculosis and Mycobacterium leprae. Transposon disruption of ponA in M. smegmatis resulted in a PBP1-deficient mutant that was sensitive to β-lactam antibiotics, was more permeable to glycine, and grew slowly in liquid culture.

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In Vivo Splicing and Functional Characterization ofMycobacterium leprae RecA

Journal of Bacteriology ◽

10.1128/jb.182.12.3590-3592.2000 ◽

2000 ◽

Vol 182 (12) ◽

pp. 3590-3592 ◽

Cited By ~ 7

Author(s):

Klaus Frischkorn ◽

Burkhard Springer ◽

Erik C. Böttger ◽

Elaine O. Davis ◽

M. Joseph Colston ◽

...

Keyword(s):

Escherichia Coli ◽

Mycobacterium Tuberculosis ◽

Homologous Recombination ◽

Mycobacterium Smegmatis ◽

Functional Characterization ◽

Mycobacterium Leprae ◽

Dna Damaging Agents

ABSTRACT The RecA proteins from Mycobacterium tuberculosis andMycobacterium leprae contain inteins. In contrast to theM. tuberculosis RecA, the M. leprae RecA is not spliced in Escherichia coli. We demonstrate here thatM. leprae RecA is functionally spliced inMycobacterium smegmatis and produces resistance toward DNA-damaging agents and homologous recombination.

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